Identification and Use of Circulating Nucleic Acid Tumor Markers

ABSTRACT

Methods for creating a selector of mutated genomic regions and for using the selector set to analyze genetic alterations in a cell-free nucleic acid sample are provided. The methods can be used to measure tumor-derived nucleic acids in a blood sample from a subject and thus to monitor the progression of disease in the subject. The methods can also be used for cancer screening, cancer diagnosis, cancer prognosis, and cancer therapy designation.

STATEMENT OF GOVERNMENTAL SUPPORT

This invention was made with Government support under contractW81XWH-12-1-0285 awarded by the Department of Defense. The Governmenthas certain rights in the invention.

BACKGROUND OF THE INVENTION

Tumors continually shed DNA into the circulation, where it is readilyaccessible (Stroun et al. (1987) Eur J Cancer Clin Oncol 23:707-712).Analysis of such cancer-derived cell-free DNA (cfDNA) has the potentialto revolutionize detection and monitoring of cancer. Noninvasive accessto malignant DNA is particularly attractive for solid tumors, whichcannot be repeatedly sampled without invasive procedures. In non-smallcell lung cancer (NSCLC), PCR-based assays have been used previously todetect recurrent point mutations in genes such as KRAS or EGFR in plasmaDNA (Taniguchi et al. (2011) Clin. Cancer Res. 17:7808-7815; Gautschi etal. (2007) Cancer Lett. 254:265-273; Kuang et al. (2009) Clin. CancerRes. 15:2630-2636; Rosell et al. (2009) N. Engl. J. Med. 361:958-967),but the majority of patients lack mutations in these genes.

Other studies have proposed identifying patient-specific chromosomalrearrangements in tumors via whole genome sequencing (WGS), followed bybreakpoint qPCR from cfDNA (Leary et al. (2010) Sci. Transl. Med.2:20ra14; McBride et al. (2010) Genes Chrom. Cancer 49:1062-1069). Whilesensitive, such methods require optimization of molecular assays foreach patient, limiting their widespread clinical application. Morerecently, several groups have reported amplicon-based deep sequencingmethods to detect cfDNA mutations in up to 6 recurrently mutated genes(Forshew et al. (2012) Sci. Transl. Med. 4:136ra168; Narayan et al.(2012) Cancer Res. 72:3492-3498; Kinde et al. (2011) Proc. Natl Acad.Sci. USA 108:9530-9535). While powerful, these approaches are limited bythe number of mutations that can be interrogated (Rachlin et al. (2005)BMC Genomics 6:102) and the inability to detect genomic fusions.

PCT International Patent Publication No. 2011/103236 describes methodsfor identifying personalized tumor markers in a cancer patient using“mate-paired” libraries. The methods are limited to monitoring somaticchromosomal rearrangements, however, and must be personalized for eachpatient, thus limiting their applicability and increasing their cost.

U.S. Patent Application Publication No. 2010/0041048 A1 describes thequantitation of tumor-specific cell-free DNA in colorectal cancerpatients using the “BEAMing” technique (Beads, Emulsion, Amplification,and Magnetics). While this technique provides high sensitivity andspecificity, this method is for single mutations and thus any givenassay can only be applied to a subset of patients and/or requirespatient-specific optimization. U.S. Patent Application Publication No.2012/0183967 A1 describes additional methods to identify and quantifygenetic variations, including the analysis of minor variants in a DNApopulation, using the “BEAMing” technique.

U.S. Patent Application Publication No. 2012/0214678 A1 describesmethods and compositions for detecting fetal nucleic acids anddetermining the fraction of cell-free fetal nucleic acid circulating ina maternal sample. While sensitive, these methods analyze polymorphismsoccurring between maternal and fetal nucleic acids rather thanpolymorphisms that result from somatic mutations in tumor cells. Inaddition, methods that detect fetal nucleic acids in maternalcirculation require much less sensitivity than methods that detect tumornucleic acids in cancer patient circulation, because fetal nucleic acidsare much more abundant than tumor nucleic acids.

U.S. Patent Application Publication Nos. 2012/0237928 A1 and2013/0034546 describe methods for determining copy number variations ofa sequence of interest in a test sample comprising a mixture of nucleicacids. While potentially applicable to the analysis of cancer, thesemethods are directed to measuring major structural changes in nucleicacids, such as translocations, deletions, and amplifications, ratherthan single nucleotide variations.

U.S. Patent Application Publication No. 2012/0264121 A1 describesmethods for estimating a genomic fraction, for example, a fetalfraction, from polymorphisms such as small base variations orinsertions-deletions. These methods do not, however, make use ofoptimized libraries of polymorphisms, such as, for example, librariescontaining recurrently-mutated genomic regions.

U.S. Patent Application Publication No. 2013/0024127 A1 describescomputer-implemented methods for calculating a percent contribution ofcell-free nucleic acids from a major source and a minor source in amixed sample. The methods do not, however, provide any advantages inidentifying or making use of optimized libraries of polymorphisms in theanalysis.

PCT International Publication No. WO 2010/141955 A2 describes methods ofdetecting cancer by analyzing panels of genes from a patient-obtainedsample and determining the mutational status of the genes in the panel.The methods rely on a relatively small number of known cancer genes,however, and they do not provide any ranking of the genes according toeffectiveness in detection of relevant mutations. In addition, themethods were unable to detect the presence of mutations in the majorityof serum samples from actual cancer patients.

There is thus a need for new and improved methods to detect and monitortumor-related nucleic acids in cancer patients.

SUMMARY OF THE INVENTION

Compositions and methods, including methods of bioinformatic analysis,are provided for the highly sensitive analysis of circulating tumor DNA(ctDNA), e.g. DNA sequences present in the blood of an individual thatare derived from tumor cells. The methods of the invention may bereferred to as CAncer Personalized Profiling by Deep Sequencing(CAPP-Seq). Tumors of particular interest are solid tumors, includingwithout limitation carcinomas, sarcomas, gliomas, lymphomas, melanomas,etc., although hematologic cancers, such as leukemias, are not excluded.

The methods of the invention combine optimized library preparationmethods with a multi-phase bioinformatics approach to design a“selector” population of DNA oligonucleotides, which correspond torecurrently mutated regions in the cancer of interest. The selectorpopulation of DNA oligonucleotides, which may be referred to as aselector set, comprises probes for a plurality of genomic regions, andis designed such that at least one mutation within the plurality ofgenomic regions is present in a majority of all subjects with thespecific cancer; and in preferred embodiments multiple mutations arepresent in a majority of all subjects with the specific cancer.

In some embodiments of the invention, methods are provided for theidentification of a selector set appropriate for a specific tumor type.Also provided are oligonucleotide compositions of selector sets, whichmay be provided adhered to a solid substrate, tagged for affinityselection, etc.; and kits containing such selector sets. Included,without limitation, is a selector set suitable for analysis of non-smallcell lung carcinoma (NSCLC). Such kits may include executableinstructions for bioinformatics analysis of the CAPP-Seq data.

In other embodiments, methods are provided for the use of a selector setin the diagnosis and monitoring of cancer in an individual patient. Insuch embodiments the selector set is used to enrich, e.g. by hybridselection, for ctDNA that corresponds to the regions of the genome thatare most likely to contain tumor-specific somatic mutations. The“selected” ctDNA is then amplified and sequenced to determine which ofthe selected genomic regions are mutated in the individual tumor. Aninitial comparison is optionally made with the individual's germline DNAsequence and/or a tumor biopsy sample from the individual. These somaticmutations provide a means of distinguishing ctDNA from germline DNA, andthus provide useful information about the presence and quantity of tumorcells in the individual.

In some embodiments, the ctDNA content in an individual's blood, orblood derivative, sample is determined at one or more time points,optionally in conjunction with a therapeutic regimen. The presence ofthe ctDNA correlates with tumor burden, and is useful in monitoringresponse to therapy, monitoring residual disease, monitoring for thepresence of metastases, monitoring total tumor burden, and the like.Although not required, for some methods CAPP-Seq may be performed inconjunction with tumor imaging methods, e.g. PET/CT scans and the like.

In other embodiments, CAPP-seq is used for cancer screening andbiopsy-free tumor genotyping, where a patient ctDNA sample is analyzedwithout reference to a biopsy sample. In some such embodiments, whereCAPP-Seq identifies a mutation in a clinically actionable target from actDNA sample, the methods include providing a therapy appropriate forthe target. Such mutations include, without limitation, rearrangementsand other mutations involving oncogenes, receptor tyrosine kinases, etc.Actionable targets may include, for example, ALK, ROS1, RET, EGFR, KRAS,and the like.

The CAPP-Seq methods may include steps of data analysis, which may beprovided as a program of instructions executable by computer andperformed by means of software components loaded into the computer. Suchmethods include the design for identification selector set for a cancerof interest. Other bioinformatics methods are provided for determiningand quantitating when circulating tumor DNA is detectable abovebackground, e.g. using an approach that integrates information contentand classes of mutation into a detection index.

Disclosed herein is a method for determining the presence of tumornucleic acids (tNA) in a cell-free nucleic acids (cfNA) sample from anindividual by detection of somatic mutations. The method may comprise(a) obtaining a cfNA sample; (b) selecting the cfNA for sequencescorresponding to a plurality of regions of mutations in a cancer ofinterest; (c) sequencing the selected cfNA; (d) determining the presenceof somatic mutations, wherein the presence of the somatic mutations maybe indicative of tumor cells present in the individual; and (e)providing the individual with an assessment of the presence of tumorcells.

The cell-free nucleic acid may be cell-free DNA (cfDNA). The cell-freenucleic acid may be cell-free RNA (cfRNA). The cell-free nucleic acidsmay be a mixture of cell-free DNA (cfDNA) and cell-free RNA (cfRNA). Thetumor nucleic acid may be a nucleic acid originating from a tumor cell.The tumor nucleic acid may be tumor-derived DNA (tDNA). The tumornucleic acid may be a circulating tumor DNA (ctDNA). The tumor nucleicacid may be tumor-derived RNA (tRNA). The tumor nucleic acid may be acirculating tumor RNA (ctRNA). The tumor nucleic acids may be a mixtureof tumor-derived DNA and tumor-derived RNA. The tumor nucleic acids maybe a mixture of ctDNA and ctRNA.

Selecting the cfNA may comprise (i) hybridizing the cell-free nucleicacid sample to a plurality of selector set probes comprising a specificbinding member; (ii) binding hybridized nucleic acids to a complementaryspecific binding member; and (iii) washing away unbound DNA.

The cfNA sample may be compared to a known tumor DNA sequence from theindividual.

The cfNA sample may be de novo analyzed for the presence of somaticmutations.

The somatic mutations may include single nucleotide variants,insertions, deletions, copy number variations, and rearrangements.

The plurality of regions of mutations may comprise at least 5, 10, 15,20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100,125, 150, 175 or 200 different genomic regions. The plurality of regionsof mutations may comprise at least 500 different genomic regions. Theplurality of genomic regions of mutations may comprise a total of from100 to 500 kb of sequence.

At least one somatic mutation may be present in at least 60%, 65%, 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% ofindividuals in a patient population for the cancer of interest.

The cancer of interest may be a leukemia. The cancer of interest may bea solid tumor. The cancer may be a carcinoma. The carcinoma may be anadenocarcinoma or a squamous cell carcinoma. The carcinoma may benon-small cell lung cancer.

The individual may be not previously diagnosed with cancer. Theindividual may be undergoing treatment for cancer.

Two or more samples may be obtained from the individual over a period oftime and compared for residual disease or tumor burden.

The method may further comprise treating the individual in accordancewith the analysis of the presence of tumor cells. The method may furthercomprise treating the individual based on the detection of the somaticmutations.

Determining the presence of somatic mutations may comprise: (i)integrating cfDNA fractions across all somatic SNVs; (ii) performing aposition-specific background adjustment; and (iii) evaluatingstatistical significance by Monte Carlo sampling of background allelesacross the selector, wherein steps (i)-(iii) are embodied as a programof instructions executable by computer and performed by means ofsoftware components loaded into the computer.

The method may further comprise analysis of insertions and/or deletionsby comparing its fractional abundance in a given cfDNA sample againstits fractional abundance in a cohort. The method may further comprisecombining the fractional abundance into a single Z-score.

The method may further comprise integrating different mutation types toestimate the significance of tumor burden quantitation.

Determining the presence of somatic mutations may be identification ofgenomic fusion events and breakpoints by the method comprising: (i)identification of discordant reads; (ii) detection of breakpoints atbase pair-resolution, and (iii) in silico validation of candidatefusions, wherein steps (i)-(iii) are embodied as a program ofinstructions executable by computer and performed by means of softwarecomponents loaded into the computer.

Determining the presence of somatic mutation may comprise the steps of(i) taking allele frequencies from a single cfDNA sample and selectinghigh quality data; (ii) testing whether a given input cfDNA allele maybe significantly different from the corresponding paired germlineallele; (iii) assembling a database of cfDNA background allelefrequencies by binomial distribution; (iv) testing whether a given inputallele differs significantly from cfDNA background at the same position,and selecting those with an average background frequency of apredetermined threshold; and (v) distinguishing tumor-derived SNVs fromremaining background noise by outlier analysis, wherein steps (i)-(v)may be embodied as a program of instructions executable by computer andperformed by means of software components loaded into the computer.

The selector set probes may comprise sequences corresponding to amutated genomic regions identified by the method comprising identifyinga plurality of genomic regions from a group of genomic regions that maybe mutated in a specific cancer.

Identifying the plurality of genomic regions may comprise for eachgenomic region in the plurality of genomic regions, ranking the genomicregion to maximize the number of all subjects with the specific cancerhaving at least one mutation within the genomic region.

Identifying the plurality of genomic regions may comprise: (i) selectinggenes known to be drivers in the cancer of interest to generate a poolof known drivers; (ii) selecting exons from known drivers with thehighest recurrence index (RI) that identify at least one new patientcompared to step (a); and repeating until no further exons meet thesecriteria; (iii) identifying remaining exons of known drivers with anRI≧30 and with SNVs covering ≧3 patients in the relevant database thatresult in the largest reduction in patients with only 1 SNV; andrepeating until no further exons meet these criteria; (iv) repeatingstep (b) using RI≧20; (v) adding in all exons from additional genespreviously predicted to harbor driver mutations; and (vi) adding forknown recurrent rearrangement the introns most frequently implicated inthe fusion event and the flanking exons, wherein steps (i)-(vi) areembodied as a program of instructions executable by computer andperformed by means of software components loaded into the computer.

The plurality of regions of mutations in a cancer of interest may beselected from the regions set forth in Table 2.

The method of Claim 27, wherein the plurality of regions of mutationsmay comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, or 100 regions set forth in Table 2.

Further disclosed herein are compositions comprising selector setprobes. The composition may comprise a set of selector set probes of atleast about 25 nucleotides in length, comprising a specific bindingmember, and comprising sequences from at least 100 regions set forth inTable 2.

The set of selector probes may comprise oligonucleotides comprisingsequences from at least 300 regions from Table 2. The set of selectorprobes may comprise oligonucleotides comprising sequences from at least500 regions from Table 2.

Further disclosed herein are populations of cell-free DNA (cfDNA). Thepopulation of cfDNA may be an enriched population. The enrichedpopulation of cfDNA may be produced by hybrid selection. Hybridselection may comprise of use of one or more selector set probes. Theselector set probes may be attached to a solid or semi-solid support.The support may comprise an array. The support may comprise a bead. Thebead may be a coated bead. The bead may be a streptavidin bead. Thesolid support may comprise a flat surface. The solid support maycomprise a slide. The solid support may comprise a glass slide.

Further disclosed herein are methods for detecting, diagnosing,prognosing, or therapy selection for a subject suffering from a diseaseor condition. The method may comprise: (a) obtaining sequenceinformation of a cell-free DNA (cfDNA) sample derived from the subject;and (b) using sequence information derived from (a) to detect cell-freenon-germline DNA (cfNG-DNA) in the sample, wherein the method may becapable of detecting a percentage of cfNG-DNA that may be less than 2%of total cfDNA.

The method may be capable of detecting a percentage of ctDNA that may beless than 1.5% of the total cfDNA. The method may be capable ofdetecting a percentage of ctDNA that may be less than 1% of the totalcfDNA. The method may be capable of detecting a percentage of ctDNA thatmay be less than 0.5% of the total cfDNA. The method may be capable ofdetecting a percentage of ctDNA that may be less than 0.1% of the totalcfDNA. The method may be capable of detecting a percentage of ctDNA thatmay be less than 0.01% of the total cfDNA. The method may be capable ofdetecting a percentage of ctDNA that may be less than 0.001% of thetotal cfDNA. The method may be capable of detecting a percentage ofctDNA that may be less than 0.0001% of the total cfDNA.

The sample may be a plasma or serum sample (sweat, breath, tears,saliva, urine, stool, amniotic fluid). The sample may be a cerebralspinal fluid sample. In some instances, the sample is not a pap smearfluid sample. In some instances, the sample is not a cyst fluid sample.In some instances, the sample is not a pancreatic fluid sample.

The sequence information may comprise information related to at least10, 20, 30, 40, 100, 200, or 300 genomic regions. The genomic regionsmay comprise genes, exonic regions, intronic regions, untranslatedregions, non-coding regions or a combination thereof. The genomicregions may comprise two or more of exonic regions, intronic regions,and untranslated regions. The genomic regions may comprise at least oneexonic region and at least one intronic region. At least 5% of thegenomic regions may comprise intronic regions. At least about 20% of thegenomic regions may comprise exonic regions.

The genomic regions may comprise less than 1.5 megabases (Mb) of thegenome. The genomic regions may comprise less than 1 Mb of the genome.The genomic regions may comprise less than 500 kilobases (kb) of thegenome. The genomic regions may comprise less than 50, 75, 100 or 350 kbof the genome. The genomic regions may comprise between 100 kb to 300 kbof the genome.

The sequence information may comprise information pertaining to 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from a selector setcomprising a plurality of genomic regions. The sequence information maycomprise information pertaining to 25, 30, 40, 50, 60, 70, 80, 90, 100or more genomic regions from a selector set comprising a plurality ofgenomic regions. The sequence information may comprise informationpertaining to a plurality of genomic regions.

The plurality of genomic regions may be based on a selector setcomprising genomic regions comprising one or more mutations present inone or more subjects from a population of cancer subjects. At leastabout 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, or 95% of the plurality of genomic regions maybe based on a selector set comprising genomic regions comprising one ormore mutations present in one or more subjects from a population ofcancer subjects.

The total size of the genomic regions of the selector set may compriseless than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb, 300 kb,250 kb, 200 kb, or 150 kb of the genome. The total size of the genomicregions of the selector set may be between 100 kb to 300 kb of thegenome.

The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected fromTable 2. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regionsselected from Table 6. The selector set may comprise 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomicregions selected from Table 7. The selector set may comprise 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or moregenomic regions selected from Table 8. The selector set may comprise 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100or more genomic regions selected from Table 9. The selector set maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70,80, 90, 100 or more genomic regions selected from Table 10. The selectorset may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50,60, 70, 80, 90, 100 or more genomic regions selected from Table 11. Theselector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table12. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20,25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selectedfrom Table 13. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regionsselected from Table 14. The selector set may comprise 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomicregions selected from Table 15. The selector set may comprise 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 ormore genomic regions selected from Table 16. The selector set maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70,80, 90, 100 or more genomic regions selected from Table 17. The selectorset may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50,60, 70, 80, 90, 100 or more genomic regions selected from Table 18. Insome instances, the subject is not suffering from a pancreatic cancer.

Obtaining sequence information of the cell-free DNA sample may compriseperforming massively parallel sequencing. Massively parallel sequencingmay be performed on a subset of a genome of cfDNA from the cfDNA sample.The subset of the genome may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of thegenome. The subset of the genome may comprise between 100 kb to 300 kbof the genome.

Obtaining sequence information of the cell-free DNA sample may compriseusing single molecule barcoding. Using single molecule barcoding maycomprise attaching barcodes comprising different sequences to nucleicacids from the cfDNA sample.

The sequence information may comprise sequence information pertaining tothe adaptors. The sequence information may comprise sequence informationpertaining to the molecular barcodes. The sequence information maycomprise sequence information pertaining to the sample indexes.

The method may comprise obtaining sequencing information of cell-freeDNA samples from two or more samples from the subject. The method maycomprise obtaining sequencing information of cell-free DNA samples fromtwo or more different subjects. The two or more samples may be the sametype of sample. The two or more samples may be two different types ofsample. The two or more samples may be obtained from the subject at thesame time point. The two or more samples may be obtained from thesubject at two or more time points. The samples from two or moredifferent subjects may be indexed and pooled together prior tosequencing.

Using the sequence information may comprise detecting one or moremutations. The one or more mutations may comprise one or more SNVs,indels, fusions, breakpoints, structural variants, variable number oftandem repeats, hypervariable regions, minisatellites, dinucleotiderepeats, trinucleotide repeats, tetranucleotide repeats, simple sequencerepeats, copy number variants or a combination thereof in selectedregions of the subject's genome. Using the sequence information maycomprise detecting one or more of SNVs, indels, copy number variants,and rearrangements in selected regions of the subject's genome. Usingthe sequence information may comprise detecting two or more of SNVs,indels, copy number variants, and rearrangements in selected regions ofthe subject's genome. Using the sequence information may comprisedetecting at least one SNV, indel, copy number variant, andrearrangement in selected regions of the subject's genome.

In some instances, detecting the one or more mutations does not involveperforming digital PCR (dPCR).

Detecting the one or more mutations may comprise applying an algorithmto the sequence information to determine a quantity of one or moregenomic regions from a selector set. The selector set may comprise aplurality of genomic regions comprising one or more mutations present inone or more cancer subjects from a population of cancer subjects. Theselector set may comprise a plurality of genomic regions comprising oneor more mutations present in at least about 60% of cancer subjects frompopulation of cancer subjects.

The cfNG-DNA may be derived from a tumor in the subject. The method mayfurther comprise detecting a cancer in the subject based on thedetection of the cfNG-DNA. The method may further comprise diagnosing acancer in the subject based on the detection of the cfNG-DNA. Diagnosingthe cancer may have a sensitivity of at least about 50%, 52%, 55%, 57%,60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the cancer mayhave a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%,67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. The method may further comprise prognosing acancer in the subject based on the detection of the cfNG-DNA. Prognosingthe cancer may have a sensitivity of at least about 50%, 52%, 55%, 57%,60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Prognosing the cancer mayhave a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%,67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. The method may further comprise determininga therapeutic regimen for the subject based on the detection of thecfNG-DNA. The method may further comprise administering an anti-cancertherapy to the subject based on the detection of the cfNG-DNA.

The cfNG-DNA may be derived from a fetus in the subject. The method mayfurther comprise diagnosing a disease or condition in the fetus based onthe detection of the cfNG-DNA. Diagnosing the disease or condition inthe fetus may have a sensitivity of at least about 50%, 52%, 55%, 57%,60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the disease orcondition in the fetus may have a specificity of at least about 50%,52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%,87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.

The cfNG-DNA may be derived from a transplanted organ, cell or tissue inthe subject. The method may further comprise diagnosing an organtransplant rejection in the subject based on the detection of thecfNG-DNA. Diagnosing the organ transplant rejection may have asensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%,70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%,95%, 96%, 97%, or 99%. Diagnosing the organ transplant rejection mayhave a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%,67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. The method may further comprise prognosing arisk of organ transplant rejection in the subject based on the detectionof the cfNG-DNA. Prognosing the risk of organ transplant rejection mayhave a sensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%,67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. Prognosing the risk of organ transplantrejection may have a specificity of at least about 50%, 52%, 55%, 57%,60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. The method may furthercomprise determining an immunosuppresive therapy for the subject basedon the detection of the cfNG-DNA. The method may further compriseadministering an immunosuppresive therapy to the subject based on thedetection of the cfNG-DNA.

Further disclosed herein are methods of diagnosing a cancer. The methodmay comprise (a) obtaining sequence information of cell-free genomic DNAderived from a sample from a subject, wherein the sequence informationmay be derived from regions that are mutated in at least 80% of apopulation of subjects afflicted with a cancer; and (b) diagnosing acancer selected from a group consisting of lung cancer, breast cancer,colorectal cancer and prostate cancer in the subject based on thesequence information, wherein the method has a sensitivity of at least80%.

The regions that are mutated may comprise a total size of less than 1.5Mb of the genome. The regions that are mutated may comprise a total sizeof less than 1 Mb of the genome. The regions that are mutated maycomprise a total size of less than 500 kb of the genome. The regionsthat are mutated may comprise a total size of less than 350 kb of thegenome. The regions that are mutated may comprise a total size of lessthan 300 kb of the genome. The regions that are mutated may comprise atotal size of less than 250 kb of the genome. The regions that aremutated may comprise a total size of less than 200 kb of the genome. Theregions that are mutated may comprise a total size of less than 150 kbof the genome. The regions that are mutated may comprise a total size ofless than 100 kb of the genome. The regions that are mutated maycomprise a total size of less than 50 kb of the genome. The regions thatare mutated may comprise a total size of less than 40 kb of the genome.The regions that are mutated may comprise a total size of less than 30kb of the genome. The regions that are mutated may comprise a total sizeof less than 20 kb of the genome. The regions that are mutated maycomprise a total size of less than 10 kb of the genome.

The regions that are mutated may comprise a total size between 100kb-300 kb of the genome. The regions that are mutated may comprise atotal size between 5 kb-200 kb of the genome. The regions that aremutated may comprise a total size between 5 kb-150 kb of the genome. Theregions that are mutated may comprise a total size between 5 kb-100 kbof the genome. The regions that are mutated may comprise a total sizebetween 5 kb-75 kb of the genome. The regions that are mutated maycomprise a total size between 1 kb-50 kb of the genome.

The sequence information may be derived from 2 or more regions. Thesequence information may be derived from 3 or more regions. The sequenceinformation may be derived from 4 or more regions. The sequenceinformation may be derived from 5 or more regions. The sequenceinformation may be derived from 6 or more regions. The sequenceinformation may be derived from 7 or more regions. The sequenceinformation may be derived from 8 or more regions. The sequenceinformation may be derived from 9 or more regions. The sequenceinformation may be derived from 10 or more regions. The sequenceinformation may be derived from 20 or more regions. The sequenceinformation may be derived from 30 or more regions. The sequenceinformation may be derived from 40 or more regions. The sequenceinformation may be derived from 50 or more regions. The sequenceinformation may be derived from 60 or more regions. The sequenceinformation may be derived from 70 or more regions. The sequenceinformation may be derived from 80 or more regions. The sequenceinformation may be derived from 90 or more regions. The sequenceinformation may be derived from 100 or more regions.

The population of subjects afflicted with the cancer may be subjectsfrom one or more databases. The one or more databases may comprise TheCancer Genome Atlas (TCGA).

The sequence information may comprise information pertaining to at leastone mutation that may be present in at least about 60% of the populationof subjects afflicted with the cancer. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 70% of the population of subjects afflictedwith the cancer. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 80% of the population of subjects afflicted with the cancer. Thesequence information may comprise information pertaining to at least onemutation that may be present in at least about 90% of the population ofsubjects afflicted with the cancer. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 95% of the population of subjects afflictedwith the cancer. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 99% of the population of subjects afflicted with the cancer.

The sequence information may be derived from regions that may be mutatedin at least 65% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 70% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 75% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 80% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 85% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 90% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 95% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 99% of the population of subjects afflicted with the cancer.

Obtaining the sequence information may comprise sequencing noncodingregions. The noncoding regions may comprise one or more lncRNA, snoRNA,siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA,uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes,GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

Alternatively, or additionally, obtaining the sequence information maycomprise sequencing protein coding regions. The protein coding regionsmay comprise one or more exons, introns, untranslated regions, or acombination thereof.

In some instances, at least one of the regions does not comprise KRAS orEGFR. In some instances, at least two of the regions do not compriseKRAS and EGFR. In some instances, at least one of the regions does notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least two of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of theregions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.In some instances, at least four of the regions do not comprise KRAS,EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

The method may further comprise detecting mutations in the regions basedon the sequencing information. Diagnosing the cancer may be based on thedetection of the mutations. The detection of at least 3 mutations may beindicative of the cancer. The detection of one or more mutations inthree or more regions may be indicative of the cancer.

The breast cancer may be a BRCA1 cancer.

The method may have a sensitivity of at least 85%, 87%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The method may have a specificity of at least 50%, 52%, 55%, 57%, 60%,62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The method may further comprise providing a computer-generated reportcomprising the diagnosis of the cancer.

Further disclosed herein are methods of determining a prognosis of acondition or disease in a subject in need thereof. The method maycomprise (a) obtaining sequence information of cell-free genomic DNAderived from a sample from a subject, wherein the sequence informationmay be derived from regions that are mutated in at least 80% of apopulation of subjects afflicted with a condition; and (b) determining aprognosis of a condition or disease in the subject based on the sequenceinformation.

The regions that are mutated may comprise a total size of less than 1.5Mb of the genome. The regions that are mutated may comprise a total sizeof less than 1 Mb of the genome. The regions that are mutated maycomprise a total size of less than 500 kb of the genome. The regionsthat are mutated may comprise a total size of less than 350 kb of thegenome. The regions that are mutated may comprise a total size of lessthan 300 kb of the genome. The regions that are mutated may comprise atotal size of less than 250 kb of the genome. The regions that aremutated may comprise a total size of less than 200 kb of the genome. Theregions that are mutated may comprise a total size of less than 150 kbof the genome. The regions that are mutated may comprise a total size ofless than 100 kb of the genome. The regions that are mutated maycomprise a total size of less than 50 kb of the genome. The regions thatare mutated may comprise a total size of less than 40 kb of the genome.The regions that are mutated may comprise a total size of less than 30kb of the genome. The regions that are mutated may comprise a total sizeof less than 20 kb of the genome. The regions that are mutated maycomprise a total size of less than 10 kb of the genome.

The regions that are mutated may comprise a total size between 100kb-300 kb of the genome. The regions that are mutated may comprise atotal size between 5 kb-200 kb of the genome. The regions that aremutated may comprise a total size between 5 kb-150 kb of the genome. Theregions that are mutated may comprise a total size between 5 kb-100 kbof the genome. The regions that are mutated may comprise a total sizebetween 5 kb-75 kb of the genome. The regions that are mutated maycomprise a total size between 1 kb-50 kb of the genome.

The sequence information may be derived from 2 or more regions. Thesequence information may be derived from 3 or more regions. The sequenceinformation may be derived from 4 or more regions. The sequenceinformation may be derived from 5 or more regions. The sequenceinformation may be derived from 6 or more regions. The sequenceinformation may be derived from 7 or more regions. The sequenceinformation may be derived from 8 or more regions. The sequenceinformation may be derived from 9 or more regions. The sequenceinformation may be derived from 10 or more regions. The sequenceinformation may be derived from 20 or more regions. The sequenceinformation may be derived from 30 or more regions. The sequenceinformation may be derived from 40 or more regions. The sequenceinformation may be derived from 50 or more regions. The sequenceinformation may be derived from 60 or more regions. The sequenceinformation may be derived from 70 or more regions. The sequenceinformation may be derived from 80 or more regions. The sequenceinformation may be derived from 90 or more regions. The sequenceinformation may be derived from 100 or more regions.

The population of subjects afflicted with the cancer may be subjectsfrom one or more databases. The one or more databases may comprise TheCancer Genome Atlas (TCGA).

The sequence information may be derived from regions that may be mutatedin at least 65% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 70% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 75% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 80% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 85% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 90% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 95% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 99% of the population of subjects afflicted with the cancer.

Obtaining the sequence information may comprise sequencing noncodingregions. The noncoding regions may comprise one or more lncRNA, snoRNA,siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA,uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes,GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

Alternatively, or additionally, obtaining the sequence information maycomprise sequencing protein coding regions. The protein coding regionsmay comprise one or more exons, introns, untranslated regions, or acombination thereof.

In some instances, at least one of the regions does not comprise KRAS orEGFR. In some instances, at least two of the regions do not compriseKRAS and EGFR. In some instances, at least one of the regions does notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least two of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of theregions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.In some instances, at least four of the regions do not comprise KRAS,EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

The method may further comprise detecting mutations in the regions basedon the sequencing information. Prognosing the condition or disease maybe based on the detection of the mutations. The detection of at least 3mutations may be indicative of an outcome of the condition or disease.The detection of one or more mutations in three or more regions may beindicative of an outcome of the condition or disease.

The condition may be a cancer. The cancer may be a solid tumor. Thesolid tumor may be non-small cell lung cancer (NSCLC). The cancer may bea breast cancer. The breast cancer may be a BRCA1 cancer. The cancer maybe a lung cancer, colorectal cancer, prostate cancer, ovarian cancer,esophageal cancer, breast cancer, lymphoma, or leukemia.

The method may have a sensitivity of at least 50%, 52%, 55%, 57%, 60%,62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The method may have a specificity of at least 50%, 52%, 55%, 57%, 60%,62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The method may further comprise providing a computer-generated reportcomprising the prognosis of the condition.

Further disclosed herein are methods of diagnosing, prognosing, ordetermining a therapeutic regimen for a subject afflicted with orsusceptible of having a cancer. The method may comprise (a) obtainingsequence information for selected regions of genomic DNA from acell-free DNA sample from the subject; (b) using the sequenceinformation to determine the presence or absence of one or moremutations in the selected regions, wherein at least 70% of a populationof subjects afflicted with the cancer have mutation(s) in the regions;and (c) providing a report with a diagnosis, prognosis or treatmentregimen to the subject, based on the presence or absence of the one ormore mutations.

The selected regions may comprise a total size of less than 1.5 Mb ofthe genome. The selected regions may comprise a total size of less than1 Mb of the genome. The selected regions may comprise a total size ofless than 500 kb of the genome. The selected regions may comprise atotal size of less than 350 kb of the genome. The selected regions maycomprise a total size of less than 300 kb of the genome. The selectedregions may comprise a total size of less than 250 kb of the genome. Theselected regions may comprise a total size of less than 200 kb of thegenome. The selected regions may comprise a total size of less than 150kb of the genome. The selected regions may comprise a total size of lessthan 100 kb of the genome. The selected regions may comprise a totalsize of less than 50 kb of the genome. The selected regions may comprisea total size of less than 40 kb of the genome. The selected regions maycomprise a total size of less than 30 kb of the genome. The selectedregions may comprise a total size of less than 20 kb of the genome. Theselected regions may comprise a total size of less than 10 kb of thegenome.

The selected regions may comprise a total size between 100 kb-300 kb ofthe genome. The selected regions may comprise a total size between 5kb-200 kb of the genome. The selected regions may comprise a total sizebetween 5 kb-150 kb of the genome. The selected regions may comprise atotal size between 5 kb-100 kb of the genome. The selected regions maycomprise a total size between 5 kb-75 kb of the genome. The selectedregions may comprise a total size between 1 kb-50 kb of the genome.

The sequence information may be derived from 2 or more regions. Thesequence information may be derived from 3 or more regions. The sequenceinformation may be derived from 4 or more regions. The sequenceinformation may be derived from 5 or more regions. The sequenceinformation may be derived from 6 or more regions. The sequenceinformation may be derived from 7 or more regions. The sequenceinformation may be derived from 8 or more regions. The sequenceinformation may be derived from 9 or more regions. The sequenceinformation may be derived from 10 or more regions. The sequenceinformation may be derived from 20 or more regions. The sequenceinformation may be derived from 30 or more regions. The sequenceinformation may be derived from 40 or more regions. The sequenceinformation may be derived from 50 or more regions. The sequenceinformation may be derived from 60 or more regions. The sequenceinformation may be derived from 70 or more regions. The sequenceinformation may be derived from 80 or more regions. The sequenceinformation may be derived from 90 or more regions. The sequenceinformation may be derived from 100 or more regions.

The population of subjects afflicted with the cancer may be subjectsfrom one or more databases. The one or more databases may comprise TheCancer Genome Atlas (TCGA).

The sequence information may be derived from regions that may be mutatedin at least 65% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 70% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 75% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 80% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 85% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 90% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 95% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 99% of the population of subjects afflicted with the cancer.

Obtaining the sequence information may comprise sequencing noncodingregions. The noncoding regions may comprise one or more lncRNA, snoRNA,siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA,uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes,GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

Alternatively, or additionally, obtaining the sequence information maycomprise sequencing protein coding regions. The protein coding regionsmay comprise one or more exons, introns, untranslated regions, or acombination thereof.

In some instances, at least one of the regions does not comprise KRAS orEGFR. In some instances, at least two of the regions do not compriseKRAS and EGFR. In some instances, at least one of the regions does notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least two of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of theregions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.In some instances, at least four of the regions do not comprise KRAS,EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

Detection of at least 3 mutations may be indicative of an outcome of thecancer. Detection of at least 4 mutations may be indicative of anoutcome of the cancer. Detection of at least 5 mutations may beindicative of an outcome of the cancer. Detection of at least 6mutations may be indicative of an outcome of the cancer.

Detection of one or more mutations in three or more regions may beindicative of an outcome of the cancer. Detection of one or moremutations in four or more regions may be indicative of an outcome of thecancer. Detection of one or more mutations in five or more regions maybe indicative of an outcome of the cancer. Detection of one or moremutations in six or more regions may be indicative of an outcome of thecancer.

The cancer may be non-small cell lung cancer (NSCLC). The cancer may bea breast cancer. The breast cancer may be a BRCA1 cancer. The cancer maybe a lung cancer, colorectal cancer, prostate cancer, ovarian cancer,esophageal cancer, breast cancer, lymphoma, or leukemia.

The method of diagnosing or prognosing the cancer may have a sensitivityof at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%,80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.The method of diagnosing or prognosing the cancer may have a specificityof at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%,80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The may further comprise administering a therapeutic drug to thesubject. The may further comprise modifying a therapeutic regimen.Modifying the therapeutic regimen may comprise terminating thetherapeutic regimen. Modifying the therapeutic regimen may compriseincreasing a dosage or frequency of the therapeutic regimen. Modifyingthe therapeutic regimen may comprise decreasing a dosage or frequency ofthe therapeutic regimen. Modifying the therapeutic regimen may comprisestarting the therapeutic regimen.

Further disclosed herein are methods of determining a therapeutic regionfor the treatment of a condition in a subject in need thereof. Themethod may comprise (a) obtaining sequence information of cell-freegenomic DNA derived from a sample from a subject, wherein the sequenceinformation may be derived from regions that are mutated in at least 80%of a population of subjects afflicted with a condition; and (b)determining a therapeutic regimen for a condition in the subject basedon the sequence information.

The regions that are mutated may comprise a total size of less than 1.5Mb of the genome. The regions that are mutated may comprise a total sizeof less than 1 Mb of the genome. The regions that are mutated maycomprise a total size of less than 500 kb of the genome. The regionsthat are mutated may comprise a total size of less than 350 kb of thegenome. The regions that are mutated may comprise a total size of lessthan 300 kb of the genome. The regions that are mutated may comprise atotal size of less than 250 kb of the genome. The regions that aremutated may comprise a total size of less than 200 kb of the genome. Theregions that are mutated may comprise a total size of less than 150 kbof the genome. The regions that are mutated may comprise a total size ofless than 100 kb of the genome. The regions that are mutated maycomprise a total size of less than 50 kb of the genome. The regions thatare mutated may comprise a total size of less than 40 kb of the genome.The regions that are mutated may comprise a total size of less than 30kb of the genome. The regions that are mutated may comprise a total sizeof less than 20 kb of the genome. The regions that are mutated maycomprise a total size of less than 10 kb of the genome.

The regions that are mutated may comprise a total size between 100kb-300 kb of the genome. The regions that are mutated may comprise atotal size between 5 kb-200 kb of the genome. The regions that aremutated may comprise a total size between 5 kb-150 kb of the genome. Theregions that are mutated may comprise a total size between 5 kb-100 kbof the genome. The regions that are mutated may comprise a total sizebetween 5 kb-75 kb of the genome. The regions that are mutated maycomprise a total size between 1 kb-50 kb of the genome.

The sequence information may be derived from 2 or more regions. Thesequence information may be derived from 3 or more regions. The sequenceinformation may be derived from 4 or more regions. The sequenceinformation may be derived from 5 or more regions. The sequenceinformation may be derived from 6 or more regions. The sequenceinformation may be derived from 7 or more regions. The sequenceinformation may be derived from 8 or more regions. The sequenceinformation may be derived from 9 or more regions. The sequenceinformation may be derived from 10 or more regions. The sequenceinformation may be derived from 20 or more regions. The sequenceinformation may be derived from 30 or more regions. The sequenceinformation may be derived from 40 or more regions. The sequenceinformation may be derived from 50 or more regions. The sequenceinformation may be derived from 60 or more regions. The sequenceinformation may be derived from 70 or more regions. The sequenceinformation may be derived from 80 or more regions. The sequenceinformation may be derived from 90 or more regions. The sequenceinformation may be derived from 100 or more regions.

The population of subjects afflicted with the cancer may be subjectsfrom one or more databases. The one or more databases may comprise TheCancer Genome Atlas (TCGA).

The sequence information may comprise information pertaining to at leastone mutation that may be present in at least about 60% of the populationof subjects afflicted with the cancer. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 70% of the population of subjects afflictedwith the cancer. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 80% of the population of subjects afflicted with the cancer. Thesequence information may comprise information pertaining to at least onemutation that may be present in at least about 90% of the population ofsubjects afflicted with the cancer. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 95% of the population of subjects afflictedwith the cancer. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 99% of the population of subjects afflicted with the cancer.

The sequence information may be derived from regions that may be mutatedin at least 65% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 70% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 75% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 80% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 85% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 90% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 95% of the population of subjects afflicted with the cancer.The sequence information may be derived from regions that may be mutatedin at least 99% of the population of subjects afflicted with the cancer.

Obtaining the sequence information may comprise sequencing noncodingregions. The noncoding regions may comprise one or more lncRNA, snoRNA,siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA,uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes,GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

Alternatively, or additionally, obtaining the sequence information maycomprise sequencing protein coding regions. The protein coding regionsmay comprise one or more exons, introns, untranslated regions, or acombination thereof.

In some instances, at least one of the regions does not comprise KRAS orEGFR. In some instances, at least two of the regions do not compriseKRAS and EGFR. In some instances, at least one of the regions does notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least two of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of theregions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.In some instances, at least four of the regions do not comprise KRAS,EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

The method may further comprise detecting mutations in the regions basedon the sequencing information. Determining the therapeutic regimen maybe based on the detection of the mutations.

The condition may be a cancer. The cancer may be a solid tumor. Thesolid tumor may be non-small cell lung cancer (NSCLC). The cancer may bea breast cancer. The breast cancer may be a BRCA1 cancer. The cancer maybe a lung cancer, colorectal cancer, prostate cancer, ovarian cancer,esophageal cancer, breast cancer, lymphoma, or leukemia.

Further disclosed herein are methods of assessing tumor burden in asubject in need thereof. The method may comprise (a) obtaining sequenceinformation on cell-free nucleic acids derived from a sample from thesubject; (b) using a computer readable medium to determine quantities ofcirculating tumor DNA (ctDNA) in the sample; (c) assessing tumor burdenbased on the quantities of ctDNA; and (d) reporting the tumor burden tothe subject or a representative of the subject.

Determining quantities of ctDNA may comprise determining absolutequantities of ctDNA. Determining quantities of ctDNA may comprisedetermining relative quantities of ctDNA. Determining quantities ofctDNA may be performed by counting sequence reads pertaining to thectDNA. Determining quantities of ctDNA may be performed by quantitativePCR. Determining quantities of ctDNA may be performed by digital PCR.Determining quantities of ctDNA may comprise counting sequencing readsof the ctDNA.

Determining quantities of ctDNA may be performed by molecular barcodingof the ctDNA. Molecular barcoding of the ctDNA may comprise attachingadaptors to one or more ends of the ctDNA. The adaptor may comprise aplurality of oligonucleotides. The adaptor may comprise one or moredeoxyribonucleotides. The adaptor may comprise ribonucleotides. Theadaptor may be single-stranded. The adaptor may be double-stranded. Theadaptor may comprise double-stranded and single-stranded portions. Forexample, the adaptor may be a Y-shaped adaptor. The adaptor may be alinear adaptor. The adaptor may be a circular adaptor. The adaptor maycomprise a molecular barcode, sample index, primer sequence, linkersequence or a combination thereof. The molecular barcode may be adjacentto the sample index. The molecular barcode may be adjacent to the primersequence. The sample index may be adjacent to the primer sequence. Alinker sequence may connect the molecular barcode to the sample index. Alinker sequence may connect the molecular barcode to the primersequence. A linker sequence may connect the sample index to the primersequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of a nucleic acid from a sample. Thenucleic acids may be DNA. The DNA may be cell-free DNA (cfDNA). The DNAmay be circulating tumor DNA (ctDNA). The nucleic acids may be RNA.Adaptors may be attached to both ends of the nucleic acid. Adaptors maybe attached to one or more ends of a single-stranded nucleic acid.Adaptors may be attached to one or more ends of a double-strandednucleic acid.

Adaptors may be attached to the nucleic acid by ligation. Ligation maybe blunt end ligation. Ligation may be sticky end ligation. Adaptors maybe attached to the nucleic acid by primer extension. Adaptors may beattached to the nucleic acid by reverse transcription. Adaptors may beattached to the nucleic acids by hybridization. Adaptors may comprise asequence that is at least partially complementary to the nucleic acid.Alternatively, in some instances, adaptors do not comprise a sequencethat is complementary to the nucleic acid.

The sequence information may comprise information related to one or moregenomic regions. The sequence information may comprise informationrelated to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 100, 200,300 genomic regions. The genomic regions may comprise genes, exonicregions, intronic regions, untranslated regions, non-coding regions or acombination thereof.

The genomic regions may comprise two or more of exonic regions, intronicregions, and untranslated regions. The genomic regions may comprise atleast one exonic region and at least one intronic region. At least 1%,2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, or 25% of the genomicregions may comprise intronic regions. At least 1%, 2%, 3%, 4%, 5%, 6%,7%, 8%, 9%, 10%, 15%, 20%, or 25% of the genomic regions may compriseuntranslated regions. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%,45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomicregions may comprise exonic regions. At least less than about 97%, 95%,93%, 90%, 87%, 85%, 83%, 80%, 75%, 70%, 65%, 60%, 55%, 50% of thegenomic regions may comprise exonic regions.

The genomic regions may comprise less than 1.5 megabases (Mb) of thegenome. The genomic regions may comprise less than 1 Mb of the genome.

The genomic regions may comprise less than 500 kilobases (kb) of thegenome.

The genomic regions may comprise less than 350 kb of the genome. Thegenomic regions may comprise less than 300 kb of the genome. The genomicregions may comprise less than 250 kb of the genome. The genomic regionsmay comprise less than 200 kb of the genome. The genomic regions maycomprise less than 150 kb of the genome. The genomic regions maycomprise less than 100 kb of the genome. The genomic regions maycomprise less than 50 kb of the genome. The genomic regions may compriseless than 40 kb, 30 kb, 20 kb, or 10 kb of the genome.

The genomic regions may comprise between 100 kb to 300 kb of the genome.The genomic regions may comprise between 100 kb to 200 kb of the genome.The genomic regions may comprise between 10 kb to 300 kb of the genome.The genomic regions may comprise between 10 kb to 300 kb of the genome.The genomic regions may comprise between 10 kb to 200 kb of the genome.The genomic regions may comprise between 10 kb to 150 kb of the genome.The genomic regions may comprise between 10 kb to 100 kb of the genome.The genomic regions may comprise between 10 kb to 75 kb of the genome.The genomic regions may comprise between 5 kb to 70 kb of the genome.The genomic regions may comprise between 1 kb to 50 kb of the genome.

The sequence information may comprise information pertaining to 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from a selector setcomprising a plurality of genomic regions. The sequence information maycomprise information pertaining to 25, 30, 40, 50, 60, 70, 80, 90, 100or more genomic regions from a selector set comprising a plurality ofgenomic regions.

The sequence information may comprise information pertaining to aplurality of genomic regions.

The plurality of genomic regions may be based on a selector setcomprising genomic regions comprising one or more mutations present inone or more subjects from a population of cancer subjects. At leastabout 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, or 95% of the plurality of genomic regions maybe based on a selector set comprising genomic regions comprising one ormore mutations present in one or more subjects from a population ofcancer subjects.

The total size of the genomic regions of the selector set may compriseless than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb, 300 kb,250 kb, 200 kb, or 150 kb of the genome. The total size of the genomicregions of the selector set may be between 100 kb to 300 kb of thegenome.

The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected fromTable 2.

Obtaining sequence information may comprise performing massivelyparallel sequencing. Massively parallel sequencing may be performed on asubset of a genome of the cell-free nucleic acids from the sample.

The subset of the genome may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, 150 kb, 100 kb,75 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, or 5 kb of the genome. Thesubset of the genome may comprise between 100 kb to 300 kb of thegenome. The subset of the genome may comprise between 100 kb to 200 kbof the genome. The subset of the genome may comprise between 10 kb to300 kb of the genome. The subset of the genome may comprise between 10kb to 200 kb of the genome. The subset of the genome may comprisebetween 10 kb to 100 kb of the genome. The subset of the genome maycomprise between 5 kb to 100 kb of the genome. The subset of the genomemay comprise between 5 kb to 70 kb of the genome. The subset of thegenome may comprise between 1 kb to 50 kb of the genome.

The method may comprise obtaining sequencing information of cell-freeDNA samples from two or more samples from the subject. The method maycomprise obtaining sequencing information of cell-free DNA samples fromtwo or more samples from two or more subjects. The two or more samplesmay be the same type of sample. The two or more samples may be twodifferent types of sample. The two or more samples may be obtained atthe same time point. The two or more samples may be obtained at two ormore time points.

Determining the quantities of ctDNA may comprise detecting one or moremutations. Determining the quantities of ctDNA may comprise detectingtwo or more different types of mutations. The types of mutationsinclude, but are not limited to, SNVs, indels, fusions, breakpoints,structural variants, variable number of tandem repeats, hypervariableregions, minisatellites, dinucleotide repeats, trinucleotide repeats,tetranucleotide repeats, simple sequence repeats, or a combinationthereof in selected regions of the subject's genome. Determining thequantities of ctDNA may comprise detecting one or more of SNVs, indels,copy number variants, and rearrangements in selected regions of thesubject's genome. Determining the quantities of ctDNA may comprisedetecting two or more of SNVs, indels, copy number variants, andrearrangements in selected regions of the subject's genome. Determiningthe quantities of ctDNA may comprise detecting at least one SNV, indel,copy number variant, and rearrangement in selected regions of thesubject's genome.

In some instances, determining the quantities of ctDNA does compriseperforming digital PCR (dPCR). Determining the quantities of ctDNA maycomprise applying an algorithm to the sequence information to determinea quantity of one or more genomic regions from a selector set.

The selector set may comprise a plurality of genomic regions comprisingone or more mutations present in one or more cancer subjects from apopulation of cancer subjects. The selector set may comprise a pluralityof genomic regions comprise two or more different types of mutationspresent in one or more cancer subjects from a population of cancersubjects. The selector set may comprise a plurality of genomic regionscomprising one or more mutations present in at least about 60% of cancersubjects from population of cancer subjects.

The representative of the subject may be a healthcare provider. Thehealthcare provider may be a nurse, physician, medical technician, orhospital personnel. The representative of the subject may be a familymember of the subject. The representative of the subject may be a legalguardian of the subject.

Further disclosed herein are methods of determining a disease state of acancer in a subject. The method may comprise (a) obtaining a quantity ofcirculating tumor DNA (ctDNA) in a sample from the subject; (b)obtaining a volume of a tumor in the subject; and (c) determining adisease state of a cancer in the subject based on a ratio of thequantity of ctDNA to the volume of the tumor. A high ctDNA to volumeratio may be indicative of radiographically occult disease. A low ctDNAto volume ratio may be indicative of non-malignant state.

The method may further comprise modifying a diagnosis or prognosis ofthe cancer based on the ratio of the quantity of the ctDNA to the volumeof the tumor. The method may comprise diagnosing a stage of the cancerbased on the ratio of the quantity of the ctDNA to the volume of thetumor. Modifying the diagnosis may comprise changing the stage of thecancer based on the ratio of the quantity of the ctDNA to the volume ofthe tumor. For example, a subject may be diagnosed with a stage IIIcancer. However, a low ratio of the quantity of the ctDNA to the volumeof the tumor may result in adjusting the diagnosis of the cancer to astage I or II cancer. Modifying a prognosis of the cancer may comprisechanging the predicted outcome or status of the cancer. For example, adoctor may predict that a cancer in the subject is in remission based onthe tumor volume. However, a high ratio of the quantity of the ctDNA tothe volume of the tumor may result in a prediction that the cancer isrecurrent.

Obtaining the volume of the tumor may comprise obtaining an image of thetumor. Obtaining the volume of the tumor may comprise obtaining a CTscan of the tumor.

Obtaining the quantity of ctDNA may comprise PCR. Obtaining the quantityof ctDNA may comprise digital PCR. Obtaining the quantity of ctDNA maycomprise quantitative PCR.

Obtaining the quantity of ctDNA may comprise obtaining sequencinginformation on the ctDNA. The sequencing information may compriseinformation relating to one or more genomic regions based on a selectorset.

Obtaining the quantity of ctDNA may comprise hybridization of the ctDNAto an array. The array may comprise a plurality of probes for selectivehybridization of one or more genomic regions based on a selector set.The selector set may comprise one or more genomic regions from Table 2.The selector set may comprise one or more genomic regions comprising oneor more mutations, wherein the one or more mutations may be present in apopulation of subjects suffering from a cancer. The selector set maycomprise a plurality of genomic regions comprising a plurality ofmutations, wherein the plurality of mutations may be present in at least60% of a population of subjects suffering from a cancer.

Further disclosed herein are methods of detecting stage I cancer in asubject in need thereof. The method may comprise (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced may be based on a selector set comprising aplurality of genomic regions; (b) using a computer readable medium todetermine a quantity of the cell-free DNA; and (c) detecting a stage Icancer in the sample based on the quantity of the cell-free DNA.

Determining the quantity of the cell-free DNA may comprise determiningabsolute quantities of the cell-free DNA. The quantity of the cell-freeDNA may be determined by counting sequencing reads pertaining to thecell-free DNA. The quantity of the cell-free DNA may be determined byquantitative PCR.

Determining quantities of cell-free DNA (cfDNA) may be performed bymolecular barcoding of the cfDNA. Molecular barcoding of the cfDNA maycomprise attaching adaptors to one or more ends of the cfDNA. Theadaptor may comprise a plurality of oligonucleotides. The adaptor maycomprise one or more deoxyribonucleotides. The adaptor may compriseribonucleotides. The adaptor may be single-stranded. The adaptor may bedouble-stranded. The adaptor may comprise double-stranded andsingle-stranded portions. For example, the adaptor may be a Y-shapedadaptor. The adaptor may be a linear adaptor. The adaptor may be acircular adaptor. The adaptor may comprise a molecular barcode, sampleindex, primer sequence, linker sequence or a combination thereof. Themolecular barcode may be adjacent to the sample index. The molecularbarcode may be adjacent to the primer sequence. The sample index may beadjacent to the primer sequence. A linker sequence may connect themolecular barcode to the sample index. A linker sequence may connect themolecular barcode to the primer sequence. A linker sequence may connectthe sample index to the primer sequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of the cfDNA. Adaptors may beattached to both ends of the cfDNA. Adaptors may be attached to one ormore ends of a single-stranded cfDNA. Adaptors may be attached to one ormore ends of a double-stranded cfDNA.

Adaptors may be attached to the cfDNA by ligation. Ligation may be bluntend ligation. Ligation may be sticky end ligation. Adaptors may beattached to the cfDNA by primer extension. Adaptors may be attached tothe cfDNA by reverse transcription. Adaptors may be attached to thecfDNA by hybridization. Adaptors may comprise a sequence that is atleast partially complementary to the cfDNA. Alternatively, in someinstances, adaptors do not comprise a sequence that is complementary tothe cfDNA.

Sequencing may comprise massively parallel sequencing. Sequencing maycomprise shotgun sequencing.

The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or moregenomic regions from Table 2.

At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, or 95% or more of the genomic regions in the selector set maybe based on genomic regions from Table 2.

The plurality of genomic regions may comprise one or more mutationspresent in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjectssuffering from the cancer.

The total size of the plurality of genomic regions of the selector setmay comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may comprise less than100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5kb, or 1 kb of a genome.

The total size of the plurality of genomic regions of the selector setmay be between 100 kb to 300 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 200 kb of a genome. The total size of the plurality of genomicregions of the selector set may be between 10 kb to 300 kb of a genome.The total size of the plurality of genomic regions of the selector setmay be between 10 kb to 200 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 10 kb to100 kb of a genome. The total size of the plurality of genomic regionsof the selector set may be between 5 kb to 100 kb of a genome. The totalsize of the plurality of genomic regions of the selector set may bebetween 5 kb to 75 kb of a genome. The total size of the plurality ofgenomic regions of the selector set may be between 5 kb to 50 kb of agenome.

The method of detecting the stage I cancer may have a sensitivity of atleast 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%,or 99% or more. The method of detecting the stage I cancer may have asensitivity of at least 60%. The method of detecting the stage I cancermay have a sensitivity of at least 70%. The method of detecting thestage I cancer may have a sensitivity of at least 80%. The method ofdetecting the stage I cancer may have a sensitivity of at least 90%. Themethod of detecting the stage I cancer may have a sensitivity of atleast 95%.

The method of detecting the stage I cancer may have a specificity of atleast 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%,or 99% or more. The method of detecting the stage I cancer may have aspecificity of at least 60%. The method of detecting the stage I cancermay have a specificity of at least 70%. The method of detecting thestage I cancer may have a specificity of at least 80%. The method ofdetecting the stage I cancer may have a specificity of at least 90%. Themethod of detecting the stage I cancer may have a specificity of atleast 95%.

The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stage Icancer. The method may detect at least 50% or more of stage I cancer.The method may detect at least 60% or more of stage I cancer. The methodmay detect at least 70% or more of stage I cancer. The method may detectat least 75% or more of stage I cancer.

Further disclosed herein are methods of detecting stage II cancer. Themethod may comprise (a) performing sequencing on cell-free DNA derivedfrom a sample, wherein the cell-free DNA to be sequenced may be based ona selector set comprising a plurality of genomic regions; (b) using acomputer readable medium to determine a quantity of the cell-free DNA;and (c) detecting a stage II cancer in the sample based on the quantityof the cell-free DNA.

Determining the quantity of the cell-free DNA may comprise determiningabsolute quantities of the cell-free DNA. The quantity of the cell-freeDNA may be determined by counting sequencing reads pertaining to thecell-free DNA. The quantity of the cell-free DNA may be determined byquantitative PCR.

Determining quantities of cell-free DNA (cfDNA) may be performed bymolecular barcoding of the cfDNA. Molecular barcoding of the cfDNA maycomprise attaching adaptors to one or more ends of the cfDNA. Theadaptor may comprise a plurality of oligonucleotides. The adaptor maycomprise one or more deoxyribonucleotides. The adaptor may compriseribonucleotides. The adaptor may be single-stranded. The adaptor may bedouble-stranded. The adaptor may comprise double-stranded andsingle-stranded portions. For example, the adaptor may be a Y-shapedadaptor. The adaptor may be a linear adaptor. The adaptor may be acircular adaptor. The adaptor may comprise a molecular barcode, sampleindex, primer sequence, linker sequence or a combination thereof. Themolecular barcode may be adjacent to the sample index. The molecularbarcode may be adjacent to the primer sequence. The sample index may beadjacent to the primer sequence. A linker sequence may connect themolecular barcode to the sample index. A linker sequence may connect themolecular barcode to the primer sequence. A linker sequence may connectthe sample index to the primer sequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of the cfDNA. Adaptors may beattached to both ends of the cfDNA. Adaptors may be attached to one ormore ends of a single-stranded cfDNA. Adaptors may be attached to one ormore ends of a double-stranded cfDNA.

Adaptors may be attached to the cfDNA by ligation. Ligation may be bluntend ligation. Ligation may be sticky end ligation. Adaptors may beattached to the cfDNA by primer extension. Adaptors may be attached tothe cfDNA by reverse transcription. Adaptors may be attached to thecfDNA by hybridization. Adaptors may comprise a sequence that is atleast partially complementary to the cfDNA. Alternatively, in someinstances, adaptors do not comprise a sequence that is complementary tothe cfDNA.

Sequencing may comprise massively parallel sequencing. Sequencing maycomprise shotgun sequencing.

The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or moregenomic regions from Table 2.

At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, or 95% or more of the genomic regions in the selector set maybe based on genomic regions from Table 2.

The plurality of genomic regions may comprise one or more mutationspresent in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjectssuffering from the cancer.

The total size of the plurality of genomic regions of the selector setmay comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may comprise less than100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5kb, or 1 kb of a genome.

The total size of the plurality of genomic regions of the selector setmay be between 100 kb to 300 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 200 kb of a genome. The total size of the plurality of genomicregions of the selector set may be between 10 kb to 300 kb of a genome.The total size of the plurality of genomic regions of the selector setmay be between 10 kb to 200 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 10 kb to100 kb of a genome. The total size of the plurality of genomic regionsof the selector set may be between 5 kb to 100 kb of a genome. The totalsize of the plurality of genomic regions of the selector set may bebetween 5 kb to 75 kb of a genome. The total size of the plurality ofgenomic regions of the selector set may be between 5 kb to 50 kb of agenome.

The method of detecting the stage II cancer may have a sensitivity of atleast 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%,or 99% or more. The method of detecting the stage II cancer may have asensitivity of at least 60%. The method of detecting the stage II cancermay have a sensitivity of at least 70%. The method of detecting thestage II cancer may have a sensitivity of at least 80%. The method ofdetecting the stage II cancer may have a sensitivity of at least 90%.The method of detecting the stage II cancer may have a sensitivity of atleast 95%.

The method of detecting the stage II cancer may have a specificity of atleast 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%,or 99% or more. The method of detecting the stage II cancer may have aspecificity of at least 60%. The method of detecting the stage II cancermay have a specificity of at least 70%. The method of detecting thestage II cancer may have a specificity of at least 80%. The method ofdetecting the stage II cancer may have a specificity of at least 90%.The method of detecting the stage II cancer may have a specificity of atleast 95%.

The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stageII cancer. The method may detect at least 50% or more of stage IIcancer. The method may detect at least 60% or more of stage II cancer.The method may detect at least 70% or more of stage II cancer. Themethod may detect at least 75% or more of stage II cancer. The methodmay detect at least 80% or more of stage II cancer. The method maydetect at least 85% or more of stage II cancer. The method may detect atleast 90% or more stage II cancer.

Further disclosed herein are methods of detecting stage III cancer in asubject in need thereof. The method may comprise (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced may be based on a selector set comprising aplurality of genomic regions; (b) using a computer readable medium todetermine a quantity of the cell-free DNA; and (c) detecting a stage IIIcancer in the sample based on the quantity of the cell-free DNA.

Determining the quantity of the cell-free DNA may comprise determiningabsolute quantities of the cell-free DNA. The quantity of the cell-freeDNA may be determined by counting sequencing reads pertaining to thecell-free DNA. The quantity of the cell-free DNA may be determined byquantitative PCR.

Determining quantities of cell-free DNA (cfDNA) may be performed bymolecular barcoding of the cfDNA. Molecular barcoding of the cfDNA maycomprise attaching adaptors to one or more ends of the cfDNA. Theadaptor may comprise a plurality of oligonucleotides. The adaptor maycomprise one or more deoxyribonucleotides. The adaptor may compriseribonucleotides. The adaptor may be single-stranded. The adaptor may bedouble-stranded. The adaptor may comprise double-stranded andsingle-stranded portions. For example, the adaptor may be a Y-shapedadaptor. The adaptor may be a linear adaptor. The adaptor may be acircular adaptor. The adaptor may comprise a molecular barcode, sampleindex, primer sequence, linker sequence or a combination thereof. Themolecular barcode may be adjacent to the sample index. The molecularbarcode may be adjacent to the primer sequence. The sample index may beadjacent to the primer sequence. A linker sequence may connect themolecular barcode to the sample index. A linker sequence may connect themolecular barcode to the primer sequence. A linker sequence may connectthe sample index to the primer sequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of the cfDNA. Adaptors may beattached to both ends of the cfDNA. Adaptors may be attached to one ormore ends of a single-stranded cfDNA. Adaptors may be attached to one ormore ends of a double-stranded cfDNA.

Adaptors may be attached to the cfDNA by ligation. Ligation may be bluntend ligation. Ligation may be sticky end ligation. Adaptors may beattached to the cfDNA by primer extension. Adaptors may be attached tothe cfDNA by reverse transcription. Adaptors may be attached to thecfDNA by hybridization. Adaptors may comprise a sequence that is atleast partially complementary to the cfDNA. Alternatively, in someinstances, adaptors do not comprise a sequence that is complementary tothe cfDNA.

Sequencing may comprise massively parallel sequencing. Sequencing maycomprise shotgun sequencing.

The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or moregenomic regions from Table 2.

At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, or 95% or more of the genomic regions in the selector set maybe based on genomic regions from Table 2.

The plurality of genomic regions may comprise one or more mutationspresent in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjectssuffering from the cancer.

The total size of the plurality of genomic regions of the selector setmay comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may comprise less than100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5kb, or 1 kb of a genome.

The total size of the plurality of genomic regions of the selector setmay be between 100 kb to 300 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 200 kb of a genome. The total size of the plurality of genomicregions of the selector set may be between 10 kb to 300 kb of a genome.The total size of the plurality of genomic regions of the selector setmay be between 10 kb to 200 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 10 kb to100 kb of a genome. The total size of the plurality of genomic regionsof the selector set may be between 5 kb to 100 kb of a genome. The totalsize of the plurality of genomic regions of the selector set may bebetween 5 kb to 75 kb of a genome. The total size of the plurality ofgenomic regions of the selector set may be between 5 kb to 50 kb of agenome.

The method of detecting the stage III cancer may have a sensitivity ofat least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%,97%, or 99% or more. The method of detecting the stage III cancer mayhave a sensitivity of at least 60%. The method of detecting the stageIII cancer may have a sensitivity of at least 70%. The method ofdetecting the stage III cancer may have a sensitivity of at least 80%.The method of detecting the stage III cancer may have a sensitivity ofat least 90%. The method of detecting the stage III cancer may have asensitivity of at least 95%.

The method of detecting the stage III cancer may have a specificity ofat least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%,97%, or 99% or more. The method of detecting the stage III cancer mayhave a specificity of at least 60%. The method of detecting the stageIII cancer may have a specificity of at least 70%. The method ofdetecting the stage III cancer may have a specificity of at least 80%.The method of detecting the stage III cancer may have a specificity ofat least 90%. The method of detecting the stage III cancer may have aspecificity of at least 95%.

The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stageIII cancer. The method may detect at least 50% or more of stage IIIcancer. The method may detect at least 60% or more of stage III cancer.The method may detect at least 70% or more of stage III cancer. Themethod may detect at least 75% or more of stage III cancer. The methodmay detect at least 80% or more of stage III cancer. The method maydetect at least 85% or more of stage III cancer. The method may detectat least 90% or more of stage III cancer.

Further disclosed herein is a method of detecting stage IV cancer in asubject in need thereof. The method may comprise (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced may be based on a selector set comprising aplurality of genomic regions; (b) using a computer readable medium todetermine a quantity of the cell-free DNA; and (c) detecting a stage IVcancer in the sample based on the quantity of the cell-free DNA.

Determining the quantity of the cell-free DNA may comprise determiningabsolute quantities of the cell-free DNA. The quantity of the cell-freeDNA may be determined by counting sequencing reads pertaining to thecell-free DNA. The quantity of the cell-free DNA may be determined byquantitative PCR.

Determining quantities of cell-free DNA (cfDNA) may be performed bymolecular barcoding of the cfDNA. Molecular barcoding of the cfDNA maycomprise attaching adaptors to one or more ends of the cfDNA. Theadaptor may comprise a plurality of oligonucleotides. The adaptor maycomprise one or more deoxyribonucleotides. The adaptor may compriseribonucleotides. The adaptor may be single-stranded. The adaptor may bedouble-stranded. The adaptor may comprise double-stranded andsingle-stranded portions. For example, the adaptor may be a Y-shapedadaptor. The adaptor may be a linear adaptor. The adaptor may be acircular adaptor. The adaptor may comprise a molecular barcode, sampleindex, primer sequence, linker sequence or a combination thereof. Themolecular barcode may be adjacent to the sample index. The molecularbarcode may be adjacent to the primer sequence. The sample index may beadjacent to the primer sequence. A linker sequence may connect themolecular barcode to the sample index. A linker sequence may connect themolecular barcode to the primer sequence. A linker sequence may connectthe sample index to the primer sequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of the cfDNA. Adaptors may beattached to both ends of the cfDNA. Adaptors may be attached to one ormore ends of a single-stranded cfDNA. Adaptors may be attached to one ormore ends of a double-stranded cfDNA.

Adaptors may be attached to the cfDNA by ligation. Ligation may be bluntend ligation. Ligation may be sticky end ligation. Adaptors may beattached to the cfDNA by primer extension. Adaptors may be attached tothe cfDNA by reverse transcription. Adaptors may be attached to thecfDNA by hybridization. Adaptors may comprise a sequence that is atleast partially complementary to the cfDNA. Alternatively, in someinstances, adaptors do not comprise a sequence that is complementary tothe cfDNA.

Sequencing may comprise massively parallel sequencing. Sequencing maycomprise shotgun sequencing.

The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or moregenomic regions from Table 2.

At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, or 95% or more of the genomic regions in the selector set maybe based on genomic regions from Table 2.

The plurality of genomic regions may comprise one or more mutationspresent in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjectssuffering from the cancer.

The total size of the plurality of genomic regions of the selector setmay comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may comprise less than100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5kb, or 1 kb of a genome.

The total size of the plurality of genomic regions of the selector setmay be between 100 kb to 300 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 200 kb of a genome. The total size of the plurality of genomicregions of the selector set may be between 10 kb to 300 kb of a genome.The total size of the plurality of genomic regions of the selector setmay be between 10 kb to 200 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 10 kb to100 kb of a genome. The total size of the plurality of genomic regionsof the selector set may be between 5 kb to 100 kb of a genome. The totalsize of the plurality of genomic regions of the selector set may bebetween 5 kb to 75 kb of a genome. The total size of the plurality ofgenomic regions of the selector set may be between 5 kb to 50 kb of agenome.

The method of detecting the stage IV cancer may have a sensitivity of atleast 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%,or 99% or more. The method of detecting the stage IV cancer may have asensitivity of at least 60%. The method of detecting the stage IV cancermay have a sensitivity of at least 70%. The method of detecting thestage IV cancer may have a sensitivity of at least 80%. The method ofdetecting the stage IV cancer may have a sensitivity of at least 90%.The method of detecting the stage IV cancer may have a sensitivity of atleast 95%.

The method of detecting the stage IV cancer may have a specificity of atleast 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%,or 99% or more. The method of detecting the stage IV cancer may have aspecificity of at least 60%. The method of detecting the stage IV cancermay have a specificity of at least 70%. The method of detecting thestage IV cancer may have a specificity of at least 80%. The method ofdetecting the stage IV cancer may have a specificity of at least 90%.The method of detecting the stage IV cancer may have a specificity of atleast 95%.

The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stageIV cancer. The method may detect at least 50% or more of stage IVcancer. The method may detect at least 60% or more of stage IV cancer.The method may detect at least 70% or more of stage IV cancer. Themethod may detect at least 75% or more of stage IV cancer. The methodmay detect at least 80% or more of stage IV cancer. The method maydetect at least 85% or more of stage IV cancer. The method may detect atleast 90% or more of stage IV cancer.

Further disclosed herein are methods of producing a selector set. Themethod may comprise (a) identifying genomic regions comprising mutationsin one or more subjects from a population of subjects suffering from thecancer; (b) ranking the genomic regions based on a Recurrence Index(RI), wherein the RI of the genomic region is determined by dividing anumber of subjects or tumors with mutations in the genomic region by asize of the genomic region; and (c) producing a selector set comprisingone or more genomic regions based on the RI.

At least a subset of the genomic regions that are ranked may be exonregions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions that are rankedmay comprise exon regions. At least 30% of the genomic regions that areranked may comprise exon regions. At least 40% of the genomic regionsthat are ranked may comprise exon regions. At least 50% of the genomicregions that are ranked may comprise exon regions. At least 60% of thegenomic regions that are ranked may comprise exon regions. Less than97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%,62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions thatare ranked may comprise exon regions. Less than 97% of the genomicregions that are ranked may comprise exon regions. Less than 92% of thegenomic regions that are ranked may comprise exon regions. Less than 84%of the genomic regions that are ranked may comprise exon regions. Lessthan 75% of the genomic regions that are ranked may comprise exonregions. Less than 65% of the genomic regions that are ranked maycomprise exon regions.

At least a subset of the genomic regions of the selector set maycomprise exon regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions ofthe selector set may comprise exon regions. At least 30% of the genomicregions of the selector set may comprise exon regions. At least 40% ofthe genomic regions of the selector set may comprise exon regions. Atleast 50% of the genomic regions of the selector set may comprise exonregions. At least 60% of the genomic regions of the selector set maycomprise exon regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%,77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40%of the genomic regions of the selector set may comprise exon regions.Less than 97% of the genomic regions of the selector set may compriseexon regions. Less than 92% of the genomic regions of the selector setmay comprise exon regions. Less than 84% of the genomic regions of theselector set may comprise exon regions. Less than 75% of the genomicregions of the selector set may comprise exon regions. Less than 65% ofthe genomic regions of the selector set may comprise exon regions.

At least a subset of the genomic regions that are ranked may be intronregions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions that are rankedmay comprise intron regions. At least 30% of the genomic regions thatare ranked may comprise intron regions. At least 40% of the genomicregions that are ranked may comprise intron regions. At least 50% of thegenomic regions that are ranked may comprise intron regions. At least60% of the genomic regions that are ranked may comprise intron regions.Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%,67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomicregions that are ranked may comprise intron regions. Less than 97% ofthe genomic regions that are ranked may comprise intron regions. Lessthan 92% of the genomic regions that are ranked may comprise intronregions. Less than 84% of the genomic regions that are ranked maycomprise intron regions. Less than 75% of the genomic regions that areranked may comprise intron regions. Less than 65% of the genomic regionsthat are ranked may comprise intron regions.

At least a subset of the genomic regions of the selector set maycomprise intron regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions ofthe selector set may comprise intron regions. At least 30% of thegenomic regions of the selector set may comprise intron regions. Atleast 40% of the genomic regions of the selector set may comprise intronregions. At least 50% of the genomic regions of the selector set maycomprise intron regions. At least 60% of the genomic regions of theselector set may comprise intron regions. Less than 97%, 95%, 92%, 90%,87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%,52%, 50%, 45%, or 40% of the genomic regions of the selector set maycomprise intron regions. Less than 97% of the genomic regions of theselector set may comprise intron regions. Less than 92% of the genomicregions of the selector set may comprise intron regions. Less than 84%of the genomic regions of the selector set may comprise intron regions.Less than 75% of the genomic regions of the selector set may compriseintron regions. Less than 65% of the genomic regions of the selector setmay comprise intron regions.

At least a subset of the genomic regions that are ranked may beuntranslated regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regionsthat are ranked may comprise untranslated regions. At least 30% of thegenomic regions that are ranked may comprise untranslated regions. Atleast 40% of the genomic regions that are ranked may compriseuntranslated regions. At least 50% of the genomic regions that areranked may comprise untranslated regions. At least 60% of the genomicregions that are ranked may comprise untranslated regions. Less than97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%,62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions thatare ranked may comprise untranslated regions. Less than 97% of thegenomic regions that are ranked may comprise untranslated regions. Lessthan 92% of the genomic regions that are ranked may compriseuntranslated regions. Less than 84% of the genomic regions that areranked may comprise untranslated regions. Less than 75% of the genomicregions that are ranked may comprise untranslated regions. Less than 65%of the genomic regions that are ranked may comprise untranslatedregions.

At least a subset of the genomic regions of the selector set maycomprise untranslated regions. At least 20%, 2%, 30%, 35%, 40%, 45%,50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomicregions of the selector set may comprise untranslated regions. At least30% of the genomic regions of the selector set may comprise untranslatedregions. At least 40% of the genomic regions of the selector set maycomprise untranslated regions. At least 50% of the genomic regions ofthe selector set may comprise untranslated regions. At least 60% of thegenomic regions of the selector set may comprise untranslated regions.Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%,67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomicregions of the selector set may comprise untranslated regions. Less than97% of the genomic regions of the selector set may comprise untranslatedregions. Less than 92% of the genomic regions of the selector set maycomprise untranslated regions. Less than 84% of the genomic regions ofthe selector set may comprise untranslated regions. Less than 75% of thegenomic regions of the selector set may comprise untranslated regions.Less than 65% of the genomic regions of the selector set may compriseuntranslated regions.

At least a subset of the genomic regions that are ranked may benon-coding regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions thatare ranked may comprise non-coding regions. At least 30% of the genomicregions that are ranked may comprise non-coding regions. At least 40% ofthe genomic regions that are ranked may comprise non-coding regions. Atleast 50% of the genomic regions that are ranked may comprise non-codingregions. At least 60% of the genomic regions that are ranked maycomprise non-coding regions. Less than 97%, 95%, 92%, 90%, 87%, 85%,82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%,45%, or 40% of the genomic regions that are ranked may comprisenon-coding regions. Less than 97% of the genomic regions that are rankedmay comprise non-coding regions. Less than 92% of the genomic regionsthat are ranked may comprise non-coding regions. Less than 84% of thegenomic regions that are ranked may comprise non-coding regions. Lessthan 75% of the genomic regions that are ranked may comprise non-codingregions. Less than 65% of the genomic regions that are ranked maycomprise non-coding regions.

At least a subset of the genomic regions of the selector set maycomprise non-coding regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomicregions of the selector set may comprise non-coding regions. At least30% of the genomic regions of the selector set may comprise non-codingregions. At least 40% of the genomic regions of the selector set maycomprise non-coding regions. At least 50% of the genomic regions of theselector set may comprise non-coding regions. At least 60% of thegenomic regions of the selector set may comprise non-coding regions.Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%,67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomicregions of the selector set may comprise non-coding regions. Less than97% of the genomic regions of the selector set may comprise non-codingregions. Less than 92% of the genomic regions of the selector set maycomprise non-coding regions. Less than 84% of the genomic regions of theselector set may comprise non-coding regions. Less than 75% of thegenomic regions of the selector set may comprise non-coding regions.Less than 65% of the genomic regions of the selector set may comprisenon-coding regions.

Producing the selector set based on the RI may comprise selectinggenomic regions that have a recurrence index in the top 60^(th),65^(th), 70^(th), 72^(nd), 75^(th), 77^(th), 80^(th), 82^(nd), 85^(th),87^(th), 90^(th), 92^(nd), 95^(th), or 97^(th) or greater percentile.Producing the selector set based on the RI may comprise selectinggenomic regions that have a recurrence index in the top 80^(th) orgreater percentile. Producing the selector set based on the RI maycomprise selecting genomic regions that have a recurrence index in thetop 70^(th) or greater percentile. Producing the selector set based onthe RI may comprise selecting genomic regions that have a recurrenceindex in the top 90^(th) or greater percentile.

Producing the selector set further may comprise selecting genomicregions that result in the largest reduction in a number of subjectswith one mutation in the genomic region.

Producing the selector set may comprise applying an algorithm to asubset of the ranked genomic regions. The algorithm may be applied 2, 3,4, 5, 6, 7, 8, 9, 10 or more times. The algorithm may be applied two ormore times. The algorithm may be applied three or more times.

Producing the selector set may comprise selecting genomic regions thatmaximize a median number of mutations per subject of the selector set.Producing the selector set may comprise selecting genomic regions thatmaximize the number of subjects in the selector set.

Producing the selector set may comprise selecting genomic regions thatminimize the total size of the genomic regions.

The selector set may comprise information pertaining to a plurality ofgenomic regions comprising one or more mutations present in at least onesubject suffering from a cancer. The selector set may compriseinformation pertaining to a plurality of genomic regions comprising 1,2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moremutations present in at least one subject suffering from a cancer. Theselector set may comprise information pertaining to a plurality ofgenomic regions comprising 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75,80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 ormore mutations present in at least one subject suffering from a cancer.

The selector set may comprise information pertaining to a plurality ofgenomic regions comprising one or more mutations present in at least onesubject suffering from a cancer. The one or more mutations within theplurality of genomic regions may be present in at least 1, 2, 3, 4, 5,6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more subjectssuffering from a cancer. The one or more mutations within the genomicregions may be present in at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200 or more subjects suffering from a cancer.

The selector set may comprise information pertaining to a plurality ofgenomic regions comprising one or more mutations present in at least onesubject suffering from a cancer. The one or more mutations within theplurality of genomic regions may be present in at least 1%, 2%, 3%, 4%,5%, 6%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% ormore subjects from a population of subjects suffering from a cancer. Theone or more mutations within the plurality of genomic regions may bepresent in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, 95% or more subjects from a population of subjectssuffering from a cancer.

The selector set may comprise sequence information pertaining to aplurality of genomic regions comprising one or more mutations present inat least one subject suffering from a cancer. The selector set maycomprise sequence information pertaining to a plurality of genomicregions comprising 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20 or more mutations present in at least one subjectsuffering from a cancer. The selector set may comprise sequenceinformation pertaining to a plurality of genomic regions comprising 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120,130, 140, 150, 160, 170, 180, 190, 200 or more mutations present in atleast one subject suffering from a cancer.

The selector set may comprise sequence information pertaining to aplurality of genomic regions comprising one or more mutations present inat least one subject suffering from a cancer. The one or more mutationswithin the plurality of genomic regions may be present in at least 1, 2,3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moresubjects suffering from a cancer. The one or more mutations within thegenomic regions may be present in at least 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170,180, 190, 200 or more subjects suffering from a cancer.

The selector set may comprise sequence information pertaining to aplurality of genomic regions comprising one or more mutations present inat least one subject suffering from a cancer. The one or more mutationswithin the plurality of genomic regions may be present in at least 1%,2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%,19%, 20% or more subjects from a population of subjects suffering from acancer. The one or more mutations within the plurality of genomicregions may be present in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more subjects from apopulation of subjects suffering from a cancer.

The selector set may comprise genomic coordinates pertaining to aplurality of genomic regions comprising one or more mutations present inat least one subject suffering from a cancer. The selector set maycomprise genomic coordinates pertaining to a plurality of genomicregions comprising 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20 or more mutations present in at least one subjectsuffering from a cancer. The selector set may comprise genomiccoordinates pertaining to a plurality of genomic regions comprising 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120,130, 140, 150, 160, 170, 180, 190, 200 or more mutations present in atleast one subject suffering from a cancer.

The selector set may comprise genomic coordinates pertaining to aplurality of genomic regions comprising one or more mutations present inat least one subject suffering from a cancer. The one or more mutationswithin the plurality of genomic regions may be present in at least 1, 2,3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moresubjects suffering from a cancer. The one or more mutations within theplurality of genomic regions may be present in at least 25, 30, 35, 40,45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140,150, 160, 170, 180, 190, 200 or more subjects suffering from a cancer.

The selector set may comprise genomic coordinates pertaining to aplurality of genomic regions comprising one or more mutations present inat least one subject suffering from a cancer. The one or more mutationswithin the plurality of genomic regions may be present in at least 1%,2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%,19%, 20% or more subjects from a population of subjects suffering from acancer. The one or more mutations within the plurality of genomicregions may be present in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more subjects from apopulation of subjects suffering from a cancer.

The selector set may comprise genomic regions comprising one or moretypes of mutations. The selector set may comprise genomic regionscomprising two or more types of mutations. The selector set may comprisegenomic regions comprising three or more types of mutations. Theselector set may comprise genomic regions comprising four or more typesof mutations. The types of mutations may include, but are not limitedto, single nucleotide variants (SNVs), insertions/deletions (indels),rearrangements, and copy number variants (CNVs).

The selector set may comprise genomic regions comprising two or moredifferent types of mutations selected from a group consisting of singlenucleotide variants (SNVs), insertions/deletions (indels),rearrangements, and copy number variants (CNVs). The selector set maycomprise genomic regions comprising three or more different types ofmutations selected from a group consisting of single nucleotide variants(SNVs), insertions/deletions (indels), rearrangements, and copy numbervariants (CNVs). The selector set may comprise genomic regionscomprising four or more different types of mutations selected from agroup consisting of single nucleotide variants (SNVs),insertions/deletions (indels), rearrangements, and copy number variants(CNVs).

The selector set may comprise a genomic region comprising at least oneSNV and a genomic region comprising at least one other type of mutation.The selector set may comprise a genomic region comprising at least oneSNV and a genomic region comprising at least one indel. The selector setmay comprise a genomic region comprising at least one SNV and a genomicregion comprising at least one rearrangement. The selector set maycomprise a genomic region comprising at least one SNV and a genomicregion comprising at least one CNV.

The selector set may comprise a genomic region comprising at least oneindel and a genomic region comprising at least one other type ofmutation. The selector set may comprise a genomic region comprising atleast one indel and a genomic region comprising at least one SNV. Theselector set may comprise a genomic region comprising at least one indeland a genomic region comprising at least one rearrangement. The selectorset may comprise a genomic region comprising at least one indel and agenomic region comprising at least one CNV.

The selector set may comprise a genomic region comprising at least onerearrangement. The selector set may comprise a genomic region comprisingat least one rearrangement and a genomic region comprising at least oneother type of mutation. The selector set may comprise a genomic regioncomprising at least one rearrangement and a genomic region comprising atleast one SNV. The selector set may comprise a genomic region comprisingat least one rearrangement and a genomic region comprising at least oneindel. The selector set may comprise a genomic region comprising atleast one rearrangement and a genomic region comprising at least oneCNV.

The selector set may comprise a genomic region comprising at least oneCNV and a genomic region comprising at least one other type of mutation.The selector set may comprise a genomic region comprising at least oneCNV and a genomic region comprising at least one SNV. The selector setmay comprise a genomic region comprising at least one CNV and a genomicregion comprising at least one indel. The selector set may comprise agenomic region comprising at least one CNV and a genomic regioncomprising at least one rearrangement.

At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%,14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of theselector set may comprise a SNV. At least about 25%, 30%, 35%, 40%, 45%,50%, 55%, or 60% of the genomic regions of the selector set may comprisea SNV. At least about 10% of the genomic regions of the selector set maycomprise a SNV. At least about 15% of the genomic regions of theselector set may comprise a SNV. At least about 20% of the genomicregions of the selector set may comprise a SNV. At least about 30% ofthe genomic regions of the selector set may comprise a SNV. At leastabout 40% of the genomic regions of the selector set may comprise a SNV.At least about 50% of the genomic regions of the selector set maycomprise a SNV. At least about 60% of the genomic regions of theselector set may comprise a SNV.

Less than 99%, 98%, 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%,72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50% of the genomic regionsof the selector set may comprise a SNV. Less than 97% of the genomicregions of the selector set may comprise a SNV. Less than 95% of thegenomic regions of the selector set may comprise a SNV. Less than 90% ofthe genomic regions of the selector set may comprise a SNV. Less than85% of the genomic regions of the selector set may comprise a SNV. Lessthan 77% of the genomic regions of the selector set may comprise a SNV.

The genomic regions of the selector set may comprise between about 10%to about 95% SNVs. The genomic regions of the selector set may comprisebetween about 10% to about 90% SNVs. The genomic regions of the selectorset may comprise between about 15% to about 95% SNVs. The genomicregions of the selector set may comprise between about 20% to about 95%SNVs. The genomic regions of the selector set may comprise between about30% to about 95% SNVs. The genomic regions of the selector set maycomprise between about 30% to about 90% SNVs. The genomic regions of theselector set may comprise between about 30% to about 85% SNVs. Thegenomic regions of the selector set may comprise between about 30% toabout 80% SNVs.

At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%,14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of theselector set may comprise an indel. At least about 25%, 30%, 35%, 40%,45%, 50%, 55%, or 60% of the genomic regions of the selector set maycomprise an indel. At least about 1% of the genomic regions of theselector set may comprise an indel. At least about 3% of the genomicregions of the selector set may comprise an indel. At least about 5% ofthe genomic regions of the selector set may comprise an indel. At leastabout 8% of the genomic regions of the selector set may comprise anindel. At least about 10% of the genomic regions of the selector set maycomprise an indel. At least about 15% of the genomic regions of theselector set may comprise an indel. At least about 30% of the genomicregions of the selector set may comprise an indel.

Less than 99%, 98%, 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%,72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50% of the genomic regionsof the selector set may comprise an indel. Less than 97% of the genomicregions of the selector set may comprise an indel. Less than 95% of thegenomic regions of the selector set may comprise an indel. Less than 90%of the genomic regions of the selector set may comprise an indel. Lessthan 85% of the genomic regions of the selector set may comprise anindel. Less than 77% of the genomic regions of the selector set maycomprise an indel.

The genomic regions of the selector set may comprise between about 10%to about 95% indels. The genomic regions of the selector set maycomprise between about 10% to about 90% indels. The genomic regions ofthe selector set may comprise between about 10% to about 85% indels. Thegenomic regions of the selector set may comprise between about 10% toabout 80% indels. The genomic regions of the selector set may comprisebetween about 10% to about 75% indels. The genomic regions of theselector set may comprise between about 10% to about 70% indels. Thegenomic regions of the selector set may comprise between about 10% toabout 60% indels. The genomic regions of the selector set may comprisebetween about 10% to about 50% indels.

At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%,14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of theselector set may comprise a rearrangement. At least about 1% of thegenomic regions of the selector set may comprise a rearrangement. Atleast about 2% of the genomic regions of the selector set may comprise arearrangement. At least about 3% of the genomic regions of the selectorset may comprise a rearrangement. At least about 4% of the genomicregions of the selector set may comprise a rearrangement. At least about5% of the genomic regions of the selector set may comprise arearrangement.

At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%,14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of theselector set may comprise a CNV. At least about 25%, 30%, 35%, 40%, 45%,50%, 55%, or 60% of the genomic regions of the selector set may comprisea CNV. At least about 1% of the genomic regions of the selector set maycomprise a CNV. At least about 3% of the genomic regions of the selectorset may comprise a CNV. At least about 5% of the genomic regions of theselector set may comprise a CNV. At least about 8% of the genomicregions of the selector set may comprise a CNV. At least about 10% ofthe genomic regions of the selector set may comprise a CNV. At leastabout 15% of the genomic regions of the selector set may comprise a CNV.At least about 30% of the genomic regions of the selector set maycomprise a CNV.

Less than 99%, 98%, 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%,72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50% of the genomic regionsof the selector set may comprise a CNV. Less than 97% of the genomicregions of the selector set may comprise a CNV. Less than 95% of thegenomic regions of the selector set may comprise a CNV. Less than 90% ofthe genomic regions of the selector set may comprise a CNV. Less than85% of the genomic regions of the selector set may comprise a CNV. Lessthan 77% of the genomic regions of the selector set may comprise a CNV.

The genomic regions of the selector set may comprise between about 5% toabout 80% CNVs. The genomic regions of the selector set may comprisebetween about 5% to about 70% CNVs. The genomic regions of the selectorset may comprise between about 5% to about 60% CNVs. The genomic regionsof the selector set may comprise between about 5% to about 50% CNVs. Thegenomic regions of the selector set may comprise between about 5% toabout 40% CNVs. The genomic regions of the selector set may comprisebetween about 5% to about 35% CNVs. The genomic regions of the selectorset may comprise between about 5% to about 30% CNVs. The genomic regionsof the selector set may comprise between about 5% to about 25% CNVs.

The selector set may be used to classify a sample from a subject. Theselector set may be used to classify 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, or 15 or more samples from a subject. The selector set may beused to classify two or more samples from a subject.

The selector set may be used to classify one or more samples from one ormore subjects. The selector set may be used to classify two or moresamples from two or more subjects. The selector set may be used toclassify a plurality of samples from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, or 20 or more subjects.

The samples may be the same type of sample. The samples may be two ormore different types of samples. The sample may be a plasma sample. Thesample may be a tumor sample. The sample may be a germline sample. Thesample may comprise tumor-derived molecules. The sample may comprisenon-tumor-derived molecules.

The selector set may classify the sample as tumor-containing. Theselector set may classify the sample as tumor-free.

The selector set may be a personalized selector set. The selector setmay be used to diagnose a cancer in a subject in need thereof. Theselector set may be used to prognosticate a status or outcome of acancer in a subject in need thereof. The selector set may be used todetermine a therapeutic regimen for treating a cancer in a subject inneed thereof.

Alternatively, the selector set may be a universal selector set. Theselector set may be used to diagnose a cancer in a plurality of subjectsin need thereof. The selector set may be used to prognosticate a statusor outcome of a cancer in a plurality of subjects in need thereof. Theselector set may be used to determine a therapeutic regimen for treatinga cancer in a plurality of subjects in need thereof.

The plurality of subjects may comprise 5, 10, 15, 20, 25, 30, 35, 40,50, 60, 70, 80, 90, or 100 or more subjects. The plurality of subjectsmay comprise 5 or more subjects. The plurality of subjects may comprise10 or more subjects. The plurality of subjects may comprise 25 or moresubjects. The plurality of subjects may comprise 50 or more subjects.The plurality of subjects may comprise 75 or more subjects. Theplurality of subjects may comprise 100 or more subjects.

The selector set may be used to classify one or more subjects based onone or more samples from the one or more subjects. The selector set maybe used to classify a subject as a responder to a therapy. The selectorset may be used to classify a subject as a non-responder to a therapy.

The selector set may be used to design a plurality of oligonucleotides.The plurality of oligonucleotides may selectively hybridize to one ormore genomic regions identified by the selector set. At least twooligonucleotides may selectively hybridize to one genomic region. Atleast three oligonucleotides may selectively hybridize to one genomicregion. At least four oligonucleotides may selectively hybridize to onegenomic region.

An oligonucleotide of the plurality of oligonucleotides may be at leastabout 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,90, 95, or 100 nucleotides in length. An oligonucleotide may be at leastabout 20 nucleotides in length. An oligonucleotide may be at least about30 nucleotides in length. An oligonucleotide may be at least about 40nucleotides in length. An oligonucleotide may be at least about 45nucleotides in length. An oligonucleotide may be at least about 50nucleotides in length.

An oligonucleotide of the plurality of oligonucleotides may be less thanor equal to 300, 275, 250, 225, 200, 190, 180, 170, 160, 150, 140, 130,125, 120, 115, 110, 105, 100, 95, 90, 85, 80, 75, or 70 nucleotides inlength. An oligonucleotide of the plurality of oligonucleotides may beless than or equal to 200 nucleotides in length. An oligonucleotide ofthe plurality of oligonucleotides may be less than or equal to 150nucleotides in length. An oligonucleotide of the plurality ofoligonucleotides may be less than or equal to 110 nucleotides in length.An oligonucleotide of the plurality of oligonucleotides may be less thanor equal to 100 nucleotides in length. An oligonucleotide of theplurality of oligonucleotides may be less than or equal to 80nucleotides in length.

An oligonucleotide of the plurality of oligonucleotides may be betweenabout 20 to 200 nucleotides in length. An oligonucleotide of theplurality of oligonucleotides may be between about 20 to 170 nucleotidesin length. An oligonucleotide of the plurality of oligonucleotides maybe between about 20 to 150 nucleotides in length. An oligonucleotide ofthe plurality of oligonucleotides may be between about 20 to 130nucleotides in length. An oligonucleotide of the plurality ofoligonucleotides may be between about 20 to 120 nucleotides in length.An oligonucleotide of the plurality of oligonucleotides may be betweenabout 30 to 150 nucleotides in length. An oligonucleotide of theplurality of oligonucleotides may be between about 30 to 120 nucleotidesin length. An oligonucleotide of the plurality of oligonucleotides maybe between about 40 to 150 nucleotides in length. An oligonucleotide ofthe plurality of oligonucleotides may be between about 40 to 120nucleotides in length. An oligonucleotide of the plurality ofoligonucleotides may be between about 50 to 150 nucleotides in length.An oligonucleotide of the plurality of oligonucleotides may be betweenabout 50 to 120 nucleotides in length.

An oligonucleotide of the plurality of oligonucleotides may be attachedto a solid support. The solid support may be a bead. The bead may be acoated bead. The bead may be a streptavidin coated bead. The solidsupport may be an array. The solid support may be a glass slide.

Further disclosed herein are methods of producing a personalizedselector set. The method may comprise (a) obtaining a genotype of atumor in a subject; (b) identifying genomic regions comprising one ormore mutations based on the genotype of the tumor; and (c) producing aselector set comprising at least one genomic region.

Obtaining the genotype of the tumor in the subject may compriseconducting a sequencing reaction on a sample from the subject.Sequencing may comprise whole genome sequencing. Sequencing may comprisewhole exome sequencing.

Sequencing may comprise use of one or more adaptors. The adaptors may beattached to one or more nucleic acids from the sample. The adaptor maycomprise a plurality of oligonucleotides. The adaptor may comprise oneor more deoxyribonucleotides. The adaptor may comprise ribonucleotides.The adaptor may be single-stranded. The adaptor may be double-stranded.The adaptor may comprise double-stranded and single-stranded portions.For example, the adaptor may be a Y-shaped adaptor. The adaptor may be alinear adaptor. The adaptor may be a circular adaptor. The adaptor maycomprise a molecular barcode, sample index, primer sequence, linkersequence or a combination thereof. The molecular barcode may be adjacentto the sample index. The molecular barcode may be adjacent to the primersequence. The sample index may be adjacent to the primer sequence. Alinker sequence may connect the molecular barcode to the sample index. Alinker sequence may connect the molecular barcode to the primersequence. A linker sequence may connect the sample index to the primersequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of a nucleic acid from a sample. Thenucleic acids may be DNA. The DNA may be cell-free DNA (cfDNA). The DNAmay be circulating tumor DNA (ctDNA). The nucleic acids may be RNA.Adaptors may be attached to both ends of the nucleic acid. Adaptors maybe attached to one or more ends of a single-stranded nucleic acid.Adaptors may be attached to one or more ends of a double-strandednucleic acid.

Adaptors may be attached to the nucleic acid by ligation. Ligation maybe blunt end ligation. Ligation may be sticky end ligation. Adaptors maybe attached to the nucleic acid by primer extension. Adaptors may beattached to the nucleic acid by reverse transcription. Adaptors may beattached to the nucleic acids by hybridization. Adaptors may comprise asequence that is at least partially complementary to the nucleic acid.Alternatively, in some instances, adaptors do not comprise a sequencethat is complementary to the nucleic acid.

Identifying genomic regions comprising one or more mutations based onthe genotype of the tumor may comprise determining a consensus sequencefor the genomic region comprising the one or more mutations. Determiningthe consensus sequence may be based on the adaptors. Determining theconsensus sequence may be based on the molecular barcode portion of theadaptor. Determining the consensus sequence may comprise analyzingsequence reads pertaining to a molecular barcode. Determining theconsensus sequence may comprise determining a percentage of sequencereads with identical sequences based on the molecular barcode.Identifying genomic regions comprising one or more mutations maycomprise producing a list of genomic regions based on a percentage ofthe consensus sequence. Producing the list of genomic regions maycomprise selecting genomic regions with at least 80%, 82%, 85%, 87%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% consensusbased on the molecular barcode. For example, sequence information may bearranged into molecular barcode families (e.g., sequences with identicalmolecular barcodes are grouped together). Analysis of a molecularbarcode family may reveal two different sequences. 1000 sequence readsmay be associated with a first sequence and 10 sequence reads may beassociated with a second sequence. The dominant sequence (e.g., thefirst sequence) may have a consensus of 99% (e.g., (1000 divided by1010) times 100%). The list of genomic regions may comprise the dominantsequence of the genomic region. The list of genomic regions may comprisegenomic regions with 90% consensus based on the molecular barcode. Thelist of genomic regions may comprise genomic regions with 95% consensusbased on the molecular barcode. The list of genomic regions may comprisegenomic regions with 98% consensus based on the molecular barcode. Thelist of genomic regions may comprise genomic regions with 100% sequenceconsensus based on the molecular barcode. Identifying genomic regionscomprising one or more mutations based on the genotype of the tumor maycomprise producing a list of genomic regions ranked by a percentage oftheir sequence consensus.

Identifying genomic regions comprising one or more mutations based onthe genotype of the tumor may comprise calculating a fractionalabundance of the genomic region. Identifying genomic regions comprisingone or more mutations based on the genotype of the tumor may comprisecalculating a fractional abundance of the genomic region from the listof genomic regions ranked by the percentage of their sequence consensus.The fractional abundance may be calculated by dividing a number ofsequence reads that pertain to a genomic region with the one or moremutations by a total number of sequence reads for the genomic regions.For example, a genomic region may comprise exon 2 of gene X. A totalnumber of sequence reads pertaining to the genomic region may be 1000,with 100 of the sequence reads containing an insertion in exon 2 of geneX. The fractional abundance of the genomic region containing theinsertion in exon 2 of gene X would be 0.1 (e.g., 100 sequence readsdivided by 1000). Identifying genomic regions comprising one or moremutations based on the genotype of the tumor may comprise producing alist of genomic regions ranked by their fractional abundance.

Producing the selector set may comprise selecting one or more genomicregions from the list of genomic regions ranked by their fractionalabundance. Producing the selector set may comprise selecting one or moregenomic regions with a fractional abundance of less than 50%, 47%, 45%,42%, 40%, 37%, 35%, 34%, 33%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%,23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%,9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1%. Producing the selector set maycomprise selecting one or more genomic regions with a fractionalabundance of less than 37%. Producing the selector set may compriseselecting one or more genomic regions with a fractional abundance ofless than 33%. Producing the selector set may comprise selecting one ormore genomic regions with a fractional abundance of less than 30%.Producing the selector set may comprise selecting one or more genomicregions with a fractional abundance of less than 27%. Producing theselector set may comprise selecting one or more genomic regions with afractional abundance of less than 25%. Producing the selector set maycomprise selecting one or more genomic regions with a fractionalabundance of between about 0.00001% to about 35%. Producing the selectorset may comprise selecting one or more genomic regions with a fractionalabundance of between about 0.00001% to about 30%. Producing the selectorset may comprise selecting one or more genomic regions with a fractionalabundance of between about 0.00001% to about 27%.

The selector set may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ormore genomic regions. The selector set may comprise one genomic region.The selector set may comprise at least 2 genomic regions. The selectorset may comprise at least 3 genomic regions.

The genomic regions of the selector set may comprise one or morepreviously unidentified mutations. The genomic regions of the selectorset may comprise 2 or more previously unidentified mutations. Thegenomic regions of the selector set may comprise 3 or more previouslyunidentified mutations. The genomic regions of the selector set maycomprise 4 or more previously unidentified mutations.

The genomic regions may comprise one or more mutations selected from agroup consisting of SNVs, indels, rearrangements, and CNVs. The genomicregions may comprise two or more mutations selected from a groupconsisting of SNVs, indels, rearrangements, and CNVs. The genomicregions may comprise three or more mutations selected from a groupconsisting of SNVs, indels, rearrangements, and CNVs. The genomicregions may comprise four or more mutations selected from a groupconsisting of SNVs, indels, rearrangements, and CNVs.

The genomic regions may comprise one or more types of mutations selectedfrom a group consisting of SNVs, indels, rearrangements, and CNVs. Thegenomic regions may comprise two or more types of mutations selectedfrom a group consisting of SNVs, indels, rearrangements, and CNVs. Thegenomic regions may comprise three or more types of mutations selectedfrom a group consisting of SNVs, indels, rearrangements, and CNVs. Thegenomic regions may comprise four or more types of mutations selectedfrom a group consisting of SNVs, indels, rearrangements, and CNVs.

Further disclosed herein are computer readable media for use in themethods disclosed herein. The computer readable medium may comprisesequence information for two or more genomic regions wherein (a) thegenomic regions may comprise one or more mutations in greater than 80%of tumors from a population of subjects afflicted with a cancer; (b) thegenomic regions represent less than 1.5 Mb of the genome; and (c) one ormore of the following (i) the condition may be not hairy cell leukemia,ovarian cancer, Waldenstrom's macroglobulinemia; (ii) a genomic regionmay comprise at least one mutation in at least one subject afflictedwith the cancer; (iii) the cancer includes two or more different typesof cancer; (iv) the two or more genomic regions may be derived from twoor more different genes; (v) the genomic regions may comprise two ormore mutations; or (vi) the two or more genomic regions may comprise atleast 10 kb.

In some instances, the condition is not hairy cell leukemia.

The genomic regions may comprise one or more mutations in greater than60% of tumors from an additional population of subjects afflicted withanother type of cancer.

The genomic regions may be derived from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20 or more different genes. The genomicregions may be derived from 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100or more different genes.

The genomic regions may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,15, 20, 25, 30, 35, 40, 45, or 50 kb. The genomic regions may compriseat least 5 kb. The genomic regions may comprise at least 10 kb. Thegenomic regions may comprise at least 50 kb.

The sequence information may comprise genomic coordinates pertaining tothe 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20or more genomic regions. The sequence information may comprise genomiccoordinates pertaining to the 25, 30, 35, 40, 45, 50, 55, 60, 65, 70,75, 80, 85, 90, 95, 100 or more genomic regions. The sequenceinformation may comprise genomic coordinates pertaining to the 125, 150,175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500 ormore genomic regions.

The sequence information may comprise a nucleic acid sequence pertainingto the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20 or more genomic regions. The sequence information may comprise anucleic acid sequence pertaining to the 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 or more genomic regions. The sequenceinformation may comprise a nucleic acid sequence pertaining to the 125,150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475,500 or more genomic regions.

The sequence information may comprise a length of the 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genomicregions. The sequence information may comprise a length of the 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more genomicregions. The sequence information may comprise a length of the 125, 150,175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500 ormore genomic regions.

Further disclosed herein are compositions for use in the methods andsystems disclosed herein. The composition may comprise a set ofoligonucleotides that selectively hybridize to a plurality of genomicregions, wherein (a) greater than 80% of tumors from a population ofcancer subjects include one or more mutations in the genomic regions;(b) the plurality of genomic regions represent less than 1.5 Mb of thegenome; and (c) the set of oligonucleotides may comprise 5 or moredifferent oligonucleotides that selectively hybridize to the pluralityof genomic regions.

An oligonucleotide of the set of oligonucleotides may comprise a tag.The tag may be biotin. The tag may be a label. The label may be afluorescent label or dye. The tag may be an adaptor.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 2. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250,300, 350, 400, 450, 500, or 525 regions from those identified in Table2. The genomic regions may comprise at least 2 regions from thoseidentified in Table 2. The genomic regions may comprise at least 20regions from those identified in Table 2. The genomic regions maycomprise at least 60 regions from those identified in Table 2. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 2. The genomic regions may comprise at least 300 regions fromthose identified in Table 2. The genomic regions may comprise at least400 regions from those identified in Table 2. The genomic regions maycomprise at least 500 regions from those identified in Table 2.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 2. At least about 5% of the genomic regionsmay be regions identified in Table 2. At least about 10% of the genomicregions may be regions identified in Table 2. At least about 20% of thegenomic regions may be regions identified in Table 2. At least about 30%of the genomic regions may be regions identified in Table 2. At leastabout 40% of the genomic regions may be regions identified in Table 2.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 6. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, or 830 regionsfrom those identified in Table 6. The genomic regions may comprise atleast 2 regions from those identified in Table 6. The genomic regionsmay comprise at least 20 regions from those identified in Table 6. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 6. The genomic regions may comprise at least 100 regions fromthose identified in Table 6. The genomic regions may comprise at least300 regions from those identified in Table 6. The genomic regions maycomprise at least 600 regions from those identified in Table 6. Thegenomic regions may comprise at least 800 regions from those identifiedin Table 6.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 6. At least about 5% of the genomic regionsmay be regions identified in Table 6. At least about 10% of the genomicregions may be regions identified in Table 6. At least about 20% of thegenomic regions may be regions identified in Table 6. At least about 30%of the genomic regions may be regions identified in Table 6. At leastabout 40% of the genomic regions may be regions identified in Table 6.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 7. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175,200, 225, 250, 275, 300, 325, 350, 375, 400, 425, or 450 regions fromthose identified in Table 7. The genomic regions may comprise at least 2regions from those identified in Table 7. The genomic regions maycomprise at least 20 regions from those identified in Table 7. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 7. The genomic regions may comprise at least 100 regions fromthose identified in Table 7. The genomic regions may comprise at least200 regions from those identified in Table 7. The genomic regions maycomprise at least 300 regions from those identified in Table 7. Thegenomic regions may comprise at least 400 regions from those identifiedin Table 7.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 7. At least about 5% of the genomic regionsmay be regions identified in Table 7. At least about 10% of the genomicregions may be regions identified in Table 7. At least about 20% of thegenomic regions may be regions identified in Table 7. At least about 30%of the genomic regions may be regions identified in Table 7. At leastabout 40% of the genomic regions may be regions identified in Table 7.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 8. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 8. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, or 1050 regions from those identified in Table 8. The genomicregions may comprise at least 2 regions from those identified in Table8. The genomic regions may comprise at least 20 regions from thoseidentified in Table 8. The genomic regions may comprise at least 60regions from those identified in Table 8. The genomic regions maycomprise at least 100 regions from those identified in Table 8. Thegenomic regions may comprise at least 300 regions from those identifiedin Table 8. The genomic regions may comprise at least 600 regions fromthose identified in Table 8. The genomic regions may comprise at least800 regions from those identified in Table 8. The genomic regions maycomprise at least 1000 regions from those identified in Table 8.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 8. At least about 5% of the genomic regionsmay be regions identified in Table 8. At least about 10% of the genomicregions may be regions identified in Table 8. At least about 20% of thegenomic regions may be regions identified in Table 8. At least about 30%of the genomic regions may be regions identified in Table 8. At leastabout 40% of the genomic regions may be regions identified in Table 8.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 9. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 9. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1100, 1200, 1300, 1400, or 1500 regions from those identifiedin Table 9. The genomic regions may comprise at least 2 regions fromthose identified in Table 9. The genomic regions may comprise at least20 regions from those identified in Table 9. The genomic regions maycomprise at least 60 regions from those identified in Table 9. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 9. The genomic regions may comprise at least 300 regions fromthose identified in Table 9. The genomic regions may comprise at least500 regions from those identified in Table 9. The genomic regions maycomprise at least 1000 regions from those identified in Table 9. Thegenomic regions may comprise at least 1300 regions from those identifiedin Table 9.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 9. At least about 5% of the genomic regionsmay be regions identified in Table 9. At least about 10% of the genomicregions may be regions identified in Table 9. At least about 20% of thegenomic regions may be regions identified in Table 9. At least about 30%of the genomic regions may be regions identified in Table 9. At leastabout 40% of the genomic regions may be regions identified in Table 9.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 10. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 10. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or330 regions from those identified in Table 10. The genomic regions maycomprise at least 2 regions from those identified in Table 10. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 10. The genomic regions may comprise at least 60 regions fromthose identified in Table 10.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 10. At least about 5% of the genomic regionsmay be regions identified in Table 10. At least about 10% of the genomicregions may be regions identified in Table 10. At least about 20% of thegenomic regions may be regions identified in Table 10. At least about30% of the genomic regions may be regions identified in Table 10. Atleast about 40% of the genomic regions may be regions identified inTable 10.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 11. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 11. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,330, 340, 350, 375, 400, 420, 440, or 460 regions from those identifiedin Table 11. The genomic regions may comprise at least 2 regions fromthose identified in Table 11. The genomic regions may comprise at least20 regions from those identified in Table 11. The genomic regions maycomprise at least 60 regions from those identified in Table 11. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 11. The genomic regions may comprise at least 200 regions fromthose identified in Table 11. The genomic regions may comprise at least300 regions from those identified in Table 11. The genomic regions maycomprise at least 400 regions from those identified in Table 11.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 11. At least about 5% of the genomic regionsmay be regions identified in Table 11. At least about 10% of the genomicregions may be regions identified in Table 11. At least about 20% of thegenomic regions may be regions identified in Table 11. At least about30% of the genomic regions may be regions identified in Table 11. Atleast about 40% of the genomic regions may be regions identified inTable 11.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 12. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 12. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,330, 340, 350, 375, 400, 420, 440, 460, 480 or 500 regions from thoseidentified in Table 12. The genomic regions may comprise at least 2regions from those identified in Table 12. The genomic regions maycomprise at least 20 regions from those identified in Table 12. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 12. The genomic regions may comprise at least 100 regions fromthose identified in Table 12. The genomic regions may comprise at least200 regions from those identified in Table 12. The genomic regions maycomprise at least 300 regions from those identified in Table 12. Thegenomic regions may comprise at least 400 regions from those identifiedin Table 12. The genomic regions may comprise at least 500 regions fromthose identified in Table 12.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 12. At least about 5% of the genomic regionsmay be regions identified in Table 12. At least about 10% of the genomicregions may be regions identified in Table 12. At least about 20% of thegenomic regions may be regions identified in Table 12. At least about30% of the genomic regions may be regions identified in Table 12. Atleast about 40% of the genomic regions may be regions identified inTable 12.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 13. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 13. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450regions from those identified in Table 13. The genomic regions maycomprise at least 2 regions from those identified in Table 13. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 13. The genomic regions may comprise at least 60 regions fromthose identified in Table 13. The genomic regions may comprise at least100 regions from those identified in Table 13. The genomic regions maycomprise at least 300 regions from those identified in Table 13. Thegenomic regions may comprise at least 500 regions from those identifiedin Table 13. The genomic regions may comprise at least 1000 regions fromthose identified in Table 13. The genomic regions may comprise at least1300 regions from those identified in Table 13.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 13. At least about 5% of the genomic regionsmay be regions identified in Table 13. At least about 10% of the genomicregions may be regions identified in Table 13. At least about 20% of thegenomic regions may be regions identified in Table 13. At least about30% of the genomic regions may be regions identified in Table 13. Atleast about 40% of the genomic regions may be regions identified inTable 13.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 14. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 14. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regionsfrom those identified in Table 14. The genomic regions may comprise atleast 2 regions from those identified in Table 14. The genomic regionsmay comprise at least 20 regions from those identified in Table 14. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 14. The genomic regions may comprise at least 100 regions fromthose identified in Table 14. The genomic regions may comprise at least300 regions from those identified in Table 14. The genomic regions maycomprise at least 500 regions from those identified in Table 14. Thegenomic regions may comprise at least 1000 regions from those identifiedin Table 14. The genomic regions may comprise at least 1100 regions fromthose identified in Table 14. The genomic regions may comprise at least1200 regions from those identified in Table 14.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 14. At least about 5% of the genomic regionsmay be regions identified in Table 14. At least about 10% of the genomicregions may be regions identified in Table 14. At least about 20% of thegenomic regions may be regions identified in Table 14. At least about30% of the genomic regions may be regions identified in Table 14. Atleast about 40% of the genomic regions may be regions identified inTable 14.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 15. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120,130, 140, 150, 160, or 170 regions from those identified in Table 15.The genomic regions may comprise at least 2 regions from thoseidentified in Table 15. The genomic regions may comprise at least 20regions from those identified in Table 15. The genomic regions maycomprise at least 60 regions from those identified in Table 15. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 15. The genomic regions may comprise at least 120 regions fromthose identified in Table 15. The genomic regions may comprise at least150 regions from those identified in Table 15.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 15. At least about 5% of the genomic regionsmay be regions identified in Table 15. At least about 10% of the genomicregions may be regions identified in Table 15. At least about 20% of thegenomic regions may be regions identified in Table 15. At least about30% of the genomic regions may be regions identified in Table 15. Atleast about 40% of the genomic regions may be regions identified inTable 15.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 16. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 16. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000,or 2050 regions from those identified in Table 16. The genomic regionsmay comprise at least 2 regions from those identified in Table 16. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 16. The genomic regions may comprise at least 60 regions fromthose identified in Table 16. The genomic regions may comprise at least100 regions from those identified in Table 16. The genomic regions maycomprise at least 300 regions from those identified in Table 16. Thegenomic regions may comprise at least 500 regions from those identifiedin Table 16. The genomic regions may comprise at least 1000 regions fromthose identified in Table 16. The genomic regions may comprise at least1200 regions from those identified in Table 16. The genomic regions maycomprise at least 1500 regions from those identified in Table 16. Thegenomic regions may comprise at least 1700 regions from those identifiedin Table 16. The genomic regions may comprise at least 2000 regions fromthose identified in Table 16.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 16. At least about 5% of the genomic regionsmay be regions identified in Table 16. At least about 10% of the genomicregions may be regions identified in Table 16. At least about 20% of thegenomic regions may be regions identified in Table 16. At least about30% of the genomic regions may be regions identified in Table 16. Atleast about 40% of the genomic regions may be regions identified inTable 16.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 17. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 17. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regionsfrom those identified in Table 17. The genomic regions may comprise atleast 2 regions from those identified in Table 17. The genomic regionsmay comprise at least 20 regions from those identified in Table 17. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 17. The genomic regions may comprise at least 100 regions fromthose identified in Table 17. The genomic regions may comprise at least300 regions from those identified in Table 17. The genomic regions maycomprise at least 500 regions from those identified in Table 17. Thegenomic regions may comprise at least 1000 regions from those identifiedin Table 17. The genomic regions may comprise at least 1050 regions fromthose identified in Table 17.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 17. At least about 5% of the genomic regionsmay be regions identified in Table 17. At least about 10% of the genomicregions may be regions identified in Table 17. At least about 20% of thegenomic regions may be regions identified in Table 17. At least about30% of the genomic regions may be regions identified in Table 17. Atleast about 40% of the genomic regions may be regions identified inTable 17.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 18. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 18. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,330, 340, 350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555regions from those identified in Table 18. The genomic regions maycomprise at least 2 regions from those identified in Table 18. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 18. The genomic regions may comprise at least 60 regions fromthose identified in Table 18. The genomic regions may comprise at least100 regions from those identified in Table 18. The genomic regions maycomprise at least 200 regions from those identified in Table 18. Thegenomic regions may comprise at least 300 regions from those identifiedin Table 18. The genomic regions may comprise at least 400 regions fromthose identified in Table 18. The genomic regions may comprise at least500 regions from those identified in Table 18.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 18. At least about 5% of the genomic regionsmay be regions identified in Table 18. At least about 10% of the genomicregions may be regions identified in Table 18. At least about 20% of thegenomic regions may be regions identified in Table 18. At least about30% of the genomic regions may be regions identified in Table 18. Atleast about 40% of the genomic regions may be regions identified inTable 18.

The set of oligonucleotides may hybridize to less than 1.5, 1.45, 1.4,1.35, 1.3, 1.25, 1.2, 1.15, 1.1, 1.05, or 1.0 Megabases (Mb) of thegenome. The set of oligonucleotides may hybridize to less than 1000,900, 800, 700, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, or 100kb of the genome. The set of oligonucleotides may hybridize to less than1.5 Megabases (Mb) of the genome. The set of oligonucleotides mayhybridize to less than 1.25 Megabases (Mb) of the genome. The set ofoligonucleotides may hybridize to less than 1 Megabases (Mb) of thegenome. The set of oligonucleotides may hybridize to less than 1000 kbof the genome. The set of oligonucleotides may hybridize to less than500 kb of the genome. The set of oligonucleotides may hybridize to lessthan 300 kb of the genome. The set of oligonucleotides may hybridize toless than 100 kb of the genome. The set of oligonucleotides may becapable of hybridizing to greater than 50 kb of the genome.

The set of oligonucleotides may be capable of hybridizing to 5, 10, 15,20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300,350, 400, 450, or 500 or more different genomic regions. The set ofoligonucleotides may be capable of hybridizing to 5 or more differentgenomic regions. The set of oligonucleotides may be capable ofhybridizing to 20 or more different genomic regions. The set ofoligonucleotides may be capable of hybridizing to 50 or more differentgenomic regions. The set of oligonucleotides may be capable ofhybridizing to 100 or more different genomic regions.

The plurality of genomic regions may comprise 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100 or moredifferent protein-coding regions. The protein-coding regions maycomprise an exon, intron, untranslated region, or a combination thereof.

The plurality of genomic regions may comprise 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100 or moredifferent non-coding regions. The non-coding regions may comprise anon-coding RNA, ribosomal RNA (rRNA), transfer RNA (tRNA), or acombination thereof.

The oligonucleotides may be attached to a solid support. The solidsupport may be a bead. The bead may be a coated bead. The bead may be astreptavidin bead. The solid support may be an array. The solid supportmay be a glass slide.

Disclosed herein are populations of circulating tumor DNA (ctDNA) foruse in any of the methods or systems disclosed herein. A population ofcirculating tumor DNA (ctDNA) may comprise ctDNA enriched by hybridselection using any of the compositions comprising the set ofoligonucleotides disclosed herein. A population of ctDNA may comprisectDNA enriched by selective hybridization of the ctDNA using the set ofoligonucleotides based on the selector sets disclosed herein. Apopulation of ctDNA may comprise ctDNA enriched by selectivehybridization using a set of oligonucleotides based on any of Tables 2and 6-18.

Further disclosed herein are arrays for use in any of the methods andsystems disclosed herein. The array may comprise a plurality ofoligonucleotides to selectively capture genomic regions, wherein thegenomic regions may comprise a plurality of mutations present in greater60% of a population of subjects suffering from a cancer.

The plurality of mutations may be present in greater 60% of anadditional population of subjects suffering from an additional type ofcancer. The plurality of mutations may be present in greater 60% of anadditional population of subjects suffering from two or more additionaltypes of cancer. The plurality of mutations may be present in greater60% of an additional population of subjects suffering from three or moreadditional types of cancer. The plurality of mutations may be present ingreater 60% of an additional population of subjects suffering from fouror more additional types of cancer.

An oligonucleotide of the set of oligonucleotides may comprise a tag.The tag may be biotin. The tag may comprise a label. The label may be afluorescent label or dye. The tag may be an adaptor. The adaptor maycomprise a molecular barcode. The adaptor may comprise a sample index.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 2. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250,300, 350, 400, 450, 500, or 525 regions from those identified in Table2. The genomic regions may comprise at least 2 regions from thoseidentified in Table 2. The genomic regions may comprise at least 20regions from those identified in Table 2. The genomic regions maycomprise at least 60 regions from those identified in Table 2. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 2. The genomic regions may comprise at least 300 regions fromthose identified in Table 2. The genomic regions may comprise at least400 regions from those identified in Table 2. The genomic regions maycomprise at least 500 regions from those identified in Table 2.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 2. At least about 5% of the genomic regionsmay be regions identified in Table 2. At least about 10% of the genomicregions may be regions identified in Table 2. At least about 20% of thegenomic regions may be regions identified in Table 2. At least about 30%of the genomic regions may be regions identified in Table 2. At leastabout 40% of the genomic regions may be regions identified in Table 2.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 6. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, or 830 regionsfrom those identified in Table 6. The genomic regions may comprise atleast 2 regions from those identified in Table 6. The genomic regionsmay comprise at least 20 regions from those identified in Table 6. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 6. The genomic regions may comprise at least 100 regions fromthose identified in Table 6. The genomic regions may comprise at least300 regions from those identified in Table 6. The genomic regions maycomprise at least 600 regions from those identified in Table 6. Thegenomic regions may comprise at least 800 regions from those identifiedin Table 6.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 6. At least about 5% of the genomic regionsmay be regions identified in Table 6. At least about 10% of the genomicregions may be regions identified in Table 6. At least about 20% of thegenomic regions may be regions identified in Table 6. At least about 30%of the genomic regions may be regions identified in Table 6. At leastabout 40% of the genomic regions may be regions identified in Table 6.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 7. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175,200, 225, 250, 275, 300, 325, 350, 375, 400, 425, or 450 regions fromthose identified in Table 7. The genomic regions may comprise at least 2regions from those identified in Table 7. The genomic regions maycomprise at least 20 regions from those identified in Table 7. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 7. The genomic regions may comprise at least 100 regions fromthose identified in Table 7. The genomic regions may comprise at least200 regions from those identified in Table 7. The genomic regions maycomprise at least 300 regions from those identified in Table 7. Thegenomic regions may comprise at least 400 regions from those identifiedin Table 7.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 7. At least about 5% of the genomic regionsmay be regions identified in Table 7. At least about 10% of the genomicregions may be regions identified in Table 7. At least about 20% of thegenomic regions may be regions identified in Table 7. At least about 30%of the genomic regions may be regions identified in Table 7. At leastabout 40% of the genomic regions may be regions identified in Table 7.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 8. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 8. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, or 1050 regions from those identified in Table 8. The genomicregions may comprise at least 2 regions from those identified in Table8. The genomic regions may comprise at least 20 regions from thoseidentified in Table 8. The genomic regions may comprise at least 60regions from those identified in Table 8. The genomic regions maycomprise at least 100 regions from those identified in Table 8. Thegenomic regions may comprise at least 300 regions from those identifiedin Table 8. The genomic regions may comprise at least 600 regions fromthose identified in Table 8. The genomic regions may comprise at least800 regions from those identified in Table 8. The genomic regions maycomprise at least 1000 regions from those identified in Table 8.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 8. At least about 5% of the genomic regionsmay be regions identified in Table 8. At least about 10% of the genomicregions may be regions identified in Table 8. At least about 20% of thegenomic regions may be regions identified in Table 8. At least about 30%of the genomic regions may be regions identified in Table 8. At leastabout 40% of the genomic regions may be regions identified in Table 8.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 9. The genomic regions may comprise at least 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 9. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1100, 1200, 1300, 1400, or 1500 regions from those identifiedin Table 9. The genomic regions may comprise at least 2 regions fromthose identified in Table 9. The genomic regions may comprise at least20 regions from those identified in Table 9. The genomic regions maycomprise at least 60 regions from those identified in Table 9. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 9. The genomic regions may comprise at least 300 regions fromthose identified in Table 9. The genomic regions may comprise at least500 regions from those identified in Table 9. The genomic regions maycomprise at least 1000 regions from those identified in Table 9. Thegenomic regions may comprise at least 1300 regions from those identifiedin Table 9.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 9. At least about 5% of the genomic regionsmay be regions identified in Table 9. At least about 10% of the genomicregions may be regions identified in Table 9. At least about 20% of thegenomic regions may be regions identified in Table 9. At least about 30%of the genomic regions may be regions identified in Table 9. At leastabout 40% of the genomic regions may be regions identified in Table 9.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 10. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 10. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or330 regions from those identified in Table 10. The genomic regions maycomprise at least 2 regions from those identified in Table 10. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 10. The genomic regions may comprise at least 60 regions fromthose identified in Table 10.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 10. At least about 5% of the genomic regionsmay be regions identified in Table 10. At least about 10% of the genomicregions may be regions identified in Table 10. At least about 20% of thegenomic regions may be regions identified in Table 10. At least about30% of the genomic regions may be regions identified in Table 10. Atleast about 40% of the genomic regions may be regions identified inTable 10.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 11. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 11. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,330, 340, 350, 375, 400, 420, 440, or 460 regions from those identifiedin Table 11. The genomic regions may comprise at least 2 regions fromthose identified in Table 11. The genomic regions may comprise at least20 regions from those identified in Table 11. The genomic regions maycomprise at least 60 regions from those identified in Table 11. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 11. The genomic regions may comprise at least 200 regions fromthose identified in Table 11. The genomic regions may comprise at least300 regions from those identified in Table 11. The genomic regions maycomprise at least 400 regions from those identified in Table 11.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 11. At least about 5% of the genomic regionsmay be regions identified in Table 11. At least about 10% of the genomicregions may be regions identified in Table 11. At least about 20% of thegenomic regions may be regions identified in Table 11. At least about30% of the genomic regions may be regions identified in Table 11. Atleast about 40% of the genomic regions may be regions identified inTable 11.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 12. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 12. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,330, 340, 350, 375, 400, 420, 440, 460, 480 or 500 regions from thoseidentified in Table 12. The genomic regions may comprise at least 2regions from those identified in Table 12. The genomic regions maycomprise at least 20 regions from those identified in Table 12. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 12. The genomic regions may comprise at least 100 regions fromthose identified in Table 12. The genomic regions may comprise at least200 regions from those identified in Table 12. The genomic regions maycomprise at least 300 regions from those identified in Table 12. Thegenomic regions may comprise at least 400 regions from those identifiedin Table 12. The genomic regions may comprise at least 500 regions fromthose identified in Table 12.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 12. At least about 5% of the genomic regionsmay be regions identified in Table 12. At least about 10% of the genomicregions may be regions identified in Table 12. At least about 20% of thegenomic regions may be regions identified in Table 12. At least about30% of the genomic regions may be regions identified in Table 12. Atleast about 40% of the genomic regions may be regions identified inTable 12.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 13. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 13. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450regions from those identified in Table 13. The genomic regions maycomprise at least 2 regions from those identified in Table 13. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 13. The genomic regions may comprise at least 60 regions fromthose identified in Table 13. The genomic regions may comprise at least100 regions from those identified in Table 13. The genomic regions maycomprise at least 300 regions from those identified in Table 13. Thegenomic regions may comprise at least 500 regions from those identifiedin Table 13. The genomic regions may comprise at least 1000 regions fromthose identified in Table 13. The genomic regions may comprise at least1300 regions from those identified in Table 13.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 13. At least about 5% of the genomic regionsmay be regions identified in Table 13. At least about 10% of the genomicregions may be regions identified in Table 13. At least about 20% of thegenomic regions may be regions identified in Table 13. At least about30% of the genomic regions may be regions identified in Table 13. Atleast about 40% of the genomic regions may be regions identified inTable 13.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 14. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 14. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regionsfrom those identified in Table 14. The genomic regions may comprise atleast 2 regions from those identified in Table 14. The genomic regionsmay comprise at least 20 regions from those identified in Table 14. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 14. The genomic regions may comprise at least 100 regions fromthose identified in Table 14. The genomic regions may comprise at least300 regions from those identified in Table 14. The genomic regions maycomprise at least 500 regions from those identified in Table 14. Thegenomic regions may comprise at least 1000 regions from those identifiedin Table 14. The genomic regions may comprise at least 1100 regions fromthose identified in Table 14. The genomic regions may comprise at least1200 regions from those identified in Table 14.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 14. At least about 5% of the genomic regionsmay be regions identified in Table 14. At least about 10% of the genomicregions may be regions identified in Table 14. At least about 20% of thegenomic regions may be regions identified in Table 14. At least about30% of the genomic regions may be regions identified in Table 14. Atleast about 40% of the genomic regions may be regions identified inTable 14.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 15. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120,130, 140, 150, 160, or 170 regions from those identified in Table 15.The genomic regions may comprise at least 2 regions from thoseidentified in Table 15. The genomic regions may comprise at least 20regions from those identified in Table 15. The genomic regions maycomprise at least 60 regions from those identified in Table 15. Thegenomic regions may comprise at least 100 regions from those identifiedin Table 15. The genomic regions may comprise at least 120 regions fromthose identified in Table 15. The genomic regions may comprise at least150 regions from those identified in Table 15.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 15. At least about 5% of the genomic regionsmay be regions identified in Table 15. At least about 10% of the genomicregions may be regions identified in Table 15. At least about 20% of thegenomic regions may be regions identified in Table 15. At least about30% of the genomic regions may be regions identified in Table 15. Atleast about 40% of the genomic regions may be regions identified inTable 15.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 16. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 16. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000,or 2050 regions from those identified in Table 16. The genomic regionsmay comprise at least 2 regions from those identified in Table 16. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 16. The genomic regions may comprise at least 60 regions fromthose identified in Table 16. The genomic regions may comprise at least100 regions from those identified in Table 16. The genomic regions maycomprise at least 300 regions from those identified in Table 16. Thegenomic regions may comprise at least 500 regions from those identifiedin Table 16. The genomic regions may comprise at least 1000 regions fromthose identified in Table 16. The genomic regions may comprise at least1200 regions from those identified in Table 16. The genomic regions maycomprise at least 1500 regions from those identified in Table 16. Thegenomic regions may comprise at least 1700 regions from those identifiedin Table 16. The genomic regions may comprise at least 2000 regions fromthose identified in Table 16.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 16. At least about 5% of the genomic regionsmay be regions identified in Table 16. At least about 10% of the genomicregions may be regions identified in Table 16. At least about 20% of thegenomic regions may be regions identified in Table 16. At least about30% of the genomic regions may be regions identified in Table 16. Atleast about 40% of the genomic regions may be regions identified inTable 16.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 17. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 17. The genomic regions may comprise at least250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900,950, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regionsfrom those identified in Table 17. The genomic regions may comprise atleast 2 regions from those identified in Table 17. The genomic regionsmay comprise at least 20 regions from those identified in Table 17. Thegenomic regions may comprise at least 60 regions from those identifiedin Table 17. The genomic regions may comprise at least 100 regions fromthose identified in Table 17. The genomic regions may comprise at least300 regions from those identified in Table 17. The genomic regions maycomprise at least 500 regions from those identified in Table 17. Thegenomic regions may comprise at least 1000 regions from those identifiedin Table 17. The genomic regions may comprise at least 1050 regions fromthose identified in Table 17.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 17. At least about 5% of the genomic regionsmay be regions identified in Table 17. At least about 10% of the genomicregions may be regions identified in Table 17. At least about 20% of thegenomic regions may be regions identified in Table 17. At least about30% of the genomic regions may be regions identified in Table 17. Atleast about 40% of the genomic regions may be regions identified inTable 17.

The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from thoseidentified in Table 18. The genomic regions may comprise at least 25,30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions fromthose identified in Table 18. The genomic regions may comprise at least125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190,195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320,330, 340, 350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555regions from those identified in Table 18. The genomic regions maycomprise at least 2 regions from those identified in Table 18. Thegenomic regions may comprise at least 20 regions from those identifiedin Table 18. The genomic regions may comprise at least 60 regions fromthose identified in Table 18. The genomic regions may comprise at least100 regions from those identified in Table 18. The genomic regions maycomprise at least 200 regions from those identified in Table 18. Thegenomic regions may comprise at least 300 regions from those identifiedin Table 18. The genomic regions may comprise at least 400 regions fromthose identified in Table 18. The genomic regions may comprise at least500 regions from those identified in Table 18.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may beregions identified in Table 18. At least about 5% of the genomic regionsmay be regions identified in Table 18. At least about 10% of the genomicregions may be regions identified in Table 18. At least about 20% of thegenomic regions may be regions identified in Table 18. At least about30% of the genomic regions may be regions identified in Table 18. Atleast about 40% of the genomic regions may be regions identified inTable 18.

The oligonucleotides may selectively capture 5, 10, 15, 20, 25, or 30 ormore different genomic regions.

The oligonucleotides may hybridize to less than 1.5, 1.47, 1.45, 1.42,1.40, 1.37, 1.35, 1.32, 1.30, 1.27, 1.25, 1.22, 1.20, 1.17, 1.15, 1.12,1.10, 1.07, 1.05, 1.02, or 1.0 Megabases (Mb) of the genome. Theoligonucleotides may hybridize to less than 1000, 900, 800, 700, 600,500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, or 10 kb of thegenome.

The oligonucleotides may be capable of hybridizing to greater than 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, or 50 kb of thegenome. The oligonucleotides may be capable of hybridizing to greaterthan 5 kb of the genome. The oligonucleotides may be capable ofhybridizing to greater than 10 kb of the genome. The oligonucleotidesmay be capable of hybridizing to greater than 30 kb of the genome. Theoligonucleotides may be capable of hybridizing to greater than 50 kb ofthe genome.

The plurality of genomic regions may comprise 2 or more differentprotein-coding regions. The plurality of genomic regions may comprise atleast 3 different protein-coding regions. The protein-coding regions maycomprise an exon, intron, untranslated region, or a combination thereof.

The plurality of genomic regions may comprise at least one non-codingregion. The non-coding region may comprise a non-coding RNA, ribosomalRNA (rRNA), transfer RNA (tRNA), or a combination thereof.

Further disclosed herein are methods of determining a quantity ofcirculating tumor DNA (ctDNA). The method may comprise (a) ligating oneor more adaptors to cell-free DNA (cfDNA) derived from a sample from asubject to produce one or more adaptor-ligated cfDNA; (b) performingsequencing on the one or more adaptor-ligated cfDNA, wherein theadaptor-ligated cfDNA to be sequenced are based on a selector setcomprising a plurality of genomic regions; and (c) using a computerreadable medium to determine a quantity of cfDNA originating from atumor based on the sequencing information obtained from theadaptor-ligated cfDNA.

In some instances, sequencing does not comprise whole genome sequencing.In some instances, sequencing does not comprise whole exome sequencing.Sequencing may comprise massively parallel sequencing.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 2. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, or525 regions from those identified in Table 2. The genomic regions of theselector set may comprise at least 2 regions from those identified inTable 2. The genomic regions of the selector set may comprise at least20 regions from those identified in Table 2. The genomic regions of theselector set may comprise at least 60 regions from those identified inTable 2. The genomic regions of the selector set may comprise at least100 regions from those identified in Table 2. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 2. The genomic regions of the selector set may comprise at least400 regions from those identified in Table 2. The genomic regions of theselector set may comprise at least 500 regions from those identified inTable 2.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 2. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 2. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 2. At least about 20% of the genomicregions of the selector set may be regions identified in Table 2. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 2. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 2.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 6. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500,550, 600, 650, 700, 750, 800, or 830 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 6. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 6. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 6. The genomic regions of theselector set may comprise at least 600 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least800 regions from those identified in Table 6.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 6. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 6. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 6. At least about 20% of the genomicregions of the selector set may be regions identified in Table 6. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 6. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 6.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 7. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300,325, 350, 375, 400, 425, or 450 regions from those identified in Table7. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 7. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 7. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 7. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 7. The genomic regions of the selector set may comprise at least200 regions from those identified in Table 7. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 7. The genomic regions of the selector set may comprise at least400 regions from those identified in Table 7.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 7. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 7. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 7. At least about 20% of the genomicregions of the selector set may be regions identified in Table 7. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 7. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 7.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 8. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 8.The genomic regions of the selector set may comprise at least 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,or 1050 regions from those identified in Table 8. The genomic regions ofthe selector set may comprise at least 2 regions from those identifiedin Table 8. The genomic regions of the selector set may comprise atleast 20 regions from those identified in Table 8. The genomic regionsof the selector set may comprise at least 60 regions from thoseidentified in Table 8. The genomic regions of the selector set maycomprise at least 100 regions from those identified in Table 8. Thegenomic regions of the selector set may comprise at least 300 regionsfrom those identified in Table 8. The genomic regions of the selectorset may comprise at least 600 regions from those identified in Table 8.The genomic regions of the selector set may comprise at least 800regions from those identified in Table 8. The genomic regions of theselector set may comprise at least 1000 regions from those identified inTable 8.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 8. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 8. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 8. At least about 20% of the genomicregions of the selector set may be regions identified in Table 8. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 8. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 8.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 9. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 9.The genomic regions of the selector set may comprise at least 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,1100, 1200, 1300, 1400, or 1500 regions from those identified in Table9. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 9. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 9. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 9. The genomic regions of theselector set may comprise at least 500 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least1000 regions from those identified in Table 9. The genomic regions ofthe selector set may comprise at least 1300 regions from thoseidentified in Table 9.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 9. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 9. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 9. At least about 20% of the genomicregions of the selector set may be regions identified in Table 9. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 9. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 9.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 10. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 10.The genomic regions of the selector set may comprise at least 125, 130,135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200,210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or 330regions from those identified in Table 10. The genomic regions of theselector set may comprise at least 2 regions from those identified inTable 10. The genomic regions of the selector set may comprise at least20 regions from those identified in Table 10. The genomic regions of theselector set may comprise at least 60 regions from those identified inTable 10.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 10. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 10. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 10. At least about 20% of the genomicregions of the selector set may be regions identified in Table 10. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 10. At least about 40% of the genomicregions of the selector set may be regions identified in Table 10.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 11. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 11.The genomic regions of the selector set may comprise at least 125, 130,135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200,210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340,350, 375, 400, 420, 440, or 460 regions from those identified in Table11. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 11. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 11. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 11. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 11. The genomic regions of the selector set may comprise at least200 regions from those identified in Table 11. The genomic regions ofthe selector set may comprise at least 300 regions from those identifiedin Table 11. The genomic regions of the selector set may comprise atleast 400 regions from those identified in Table 11.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 11. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 11. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 11. At least about 20% of the genomicregions of the selector set may be regions identified in Table 11. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 11. At least about 40% of the genomicregions of the selector set may be regions identified in Table 11.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 12. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 12.The genomic regions of the selector set may comprise at least 125, 130,135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200,210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340,350, 375, 400, 420, 440, 460, 480 or 500 regions from those identifiedin Table 12. The genomic regions of the selector set may comprise atleast 2 regions from those identified in Table 12. The genomic regionsof the selector set may comprise at least 20 regions from thoseidentified in Table 12. The genomic regions of the selector set maycomprise at least 60 regions from those identified in Table 12. Thegenomic regions of the selector set may comprise at least 100 regionsfrom those identified in Table 12. The genomic regions of the selectorset may comprise at least 200 regions from those identified in Table 12.The genomic regions of the selector set may comprise at least 300regions from those identified in Table 12. The genomic regions of theselector set may comprise at least 400 regions from those identified inTable 12. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 12.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 12. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 12. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 12. At least about 20% of the genomicregions of the selector set may be regions identified in Table 12. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 12. At least about 40% of the genomicregions of the selector set may be regions identified in Table 12.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 13. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 13.The genomic regions of the selector set may comprise at least 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450 regions fromthose identified in Table 13. The genomic regions of the selector setmay comprise at least 2 regions from those identified in Table 13. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 13. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 13.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 13. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 13. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 13. The genomic regions ofthe selector set may comprise at least 1000 regions from thoseidentified in Table 13. The genomic regions of the selector set maycomprise at least 1300 regions from those identified in Table 13.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 13. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 13. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 13. At least about 20% of the genomicregions of the selector set may be regions identified in Table 13. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 13. At least about 40% of the genomicregions of the selector set may be regions identified in Table 13.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 14. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 14.The genomic regions of the selector set may comprise at least 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regions from thoseidentified in Table 14. The genomic regions of the selector set maycomprise at least 2 regions from those identified in Table 14. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 14. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 14.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 14. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 14. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 14. The genomic regions ofthe selector set may comprise at least 1000 regions from thoseidentified in Table 14. The genomic regions of the selector set maycomprise at least 1100 regions from those identified in Table 14. Thegenomic regions of the selector set may comprise at least 1200 regionsfrom those identified in Table 14.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 14. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 14. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 14. At least about 20% of the genomicregions of the selector set may be regions identified in Table 14. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 14. At least about 40% of the genomicregions of the selector set may be regions identified in Table 14.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 15. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, or 170regions from those identified in Table 15. The genomic regions of theselector set may comprise at least 2 regions from those identified inTable 15. The genomic regions of the selector set may comprise at least20 regions from those identified in Table 15. The genomic regions of theselector set may comprise at least 60 regions from those identified inTable 15. The genomic regions of the selector set may comprise at least100 regions from those identified in Table 15. The genomic regions ofthe selector set may comprise at least 120 regions from those identifiedin Table 15. The genomic regions of the selector set may comprise atleast 150 regions from those identified in Table 15.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 15. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 15. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 15. At least about 20% of the genomicregions of the selector set may be regions identified in Table 15. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 15. At least about 40% of the genomicregions of the selector set may be regions identified in Table 15.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 16. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 16.The genomic regions of the selector set may comprise at least 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or 2050regions from those identified in Table 16. The genomic regions of theselector set may comprise at least 2 regions from those identified inTable 16. The genomic regions of the selector set may comprise at least20 regions from those identified in Table 16. The genomic regions of theselector set may comprise at least 60 regions from those identified inTable 16. The genomic regions of the selector set may comprise at least100 regions from those identified in Table 16. The genomic regions ofthe selector set may comprise at least 300 regions from those identifiedin Table 16. The genomic regions of the selector set may comprise atleast 500 regions from those identified in Table 16. The genomic regionsof the selector set may comprise at least 1000 regions from thoseidentified in Table 16. The genomic regions of the selector set maycomprise at least 1200 regions from those identified in Table 16. Thegenomic regions of the selector set may comprise at least 1500 regionsfrom those identified in Table 16. The genomic regions of the selectorset may comprise at least 1700 regions from those identified in Table16. The genomic regions of the selector set may comprise at least 2000regions from those identified in Table 16.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 16. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 16. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 16. At least about 20% of the genomicregions of the selector set may be regions identified in Table 16. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 16. At least about 40% of the genomicregions of the selector set may be regions identified in Table 16.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 17. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 17.The genomic regions of the selector set may comprise at least 250, 300,350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000,1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regions from thoseidentified in Table 17. The genomic regions of the selector set maycomprise at least 2 regions from those identified in Table 17. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 17. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 17.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 17. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 17. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 17. The genomic regions ofthe selector set may comprise at least 1000 regions from thoseidentified in Table 17. The genomic regions of the selector set maycomprise at least 1050 regions from those identified in Table 17.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 17. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 17. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 17. At least about 20% of the genomicregions of the selector set may be regions identified in Table 17. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 17. At least about 40% of the genomicregions of the selector set may be regions identified in Table 17.

The genomic regions of the selector set may comprise at least 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or moreregions from those identified in Table 18. The genomic regions of theselector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65,70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 18.The genomic regions of the selector set may comprise at least 125, 130,135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200,210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340,350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555 regions fromthose identified in Table 18. The genomic regions of the selector setmay comprise at least 2 regions from those identified in Table 18. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 18. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 18.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 18. The genomic regions of theselector set may comprise at least 200 regions from those identified inTable 18. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 18. The genomic regions ofthe selector set may comprise at least 400 regions from those identifiedin Table 18. The genomic regions of the selector set may comprise atleast 500 regions from those identified in Table 18.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 18. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 18. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 18. At least about 20% of the genomicregions of the selector set may be regions identified in Table 18. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 18. At least about 40% of the genomicregions of the selector set may be regions identified in Table 18.

The plurality of genomic regions may comprise one or more mutationspresent in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjectssuffering from the cancer. The plurality of genomic regions may compriseone or more mutations present in at least 60% or more of a population ofsubjects suffering from the cancer. The plurality of genomic regions maycomprise one or more mutations present in at least 72% or more of apopulation of subjects suffering from the cancer. The plurality ofgenomic regions may comprise one or more mutations present in at least80% or more of a population of subjects suffering from the cancer.

The total size of the plurality of genomic regions of the selector setmay comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may comprise less than1.5 Mb of a genome. The total size of the plurality of genomic regionsof the selector set may comprise less than 1 Mb of a genome. The totalsize of the plurality of genomic regions of the selector set maycomprise less than 500 kb of a genome. The total size of the pluralityof genomic regions of the selector set may comprise less than 300 kb ofa genome. The total size of the plurality of genomic regions of theselector set may comprise less than 100, 90, 80, 70, 60, 50, 40, 30, 20,10 or 5 kb of a genome. The total size of the plurality of genomicregions of the selector set may comprise less than 100 kb of a genome.The total size of the plurality of genomic regions of the selector setmay comprise less than 75 kb of a genome. The total size of theplurality of genomic regions of the selector set may comprise less than50 kb of a genome.

The total size of the plurality of genomic regions of the selector setmay be between 100 kb to 1000 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 500 kb of a genome. The total size of the plurality of genomicregions of the selector set may be between 100 kb to 300 kb of a genome.The total size of the plurality of genomic regions of the selector setmay be between 5 kb to 500 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 5 kb to300 kb of a genome. The total size of the plurality of genomic regionsof the selector set may be between 5 kb to 200 kb of a genome. The totalsize of the plurality of genomic regions of the selector set may bebetween 1 kb to 100 kb of a genome. The total size of the plurality ofgenomic regions of the selector set may be between 1 kb to 50 kb of agenome.

Further disclosed herein are methods of preparing a library forsequencing. The method may comprise (a) conducting an amplificationreaction on cell-free DNA (cfDNA) derived from a sample to produce aplurality of amplicons, wherein the amplification reaction may comprise20 or fewer amplification cycles; and (b) producing a library forsequencing, the library comprising the plurality of amplicons.

The amplification reaction may comprise 19, 18, 17, 16, 15, 14, 13, 12,11, or 10 or fewer amplification cycles. The amplification reaction maycomprise 15 or fewer amplification cycles.

The method may further comprise attaching adaptors to one or more endsof the cfDNA. The adaptor may comprise a plurality of oligonucleotides.The adaptor may comprise one or more deoxyribonucleotides. The adaptormay comprise ribonucleotides. The adaptor may be single-stranded. Theadaptor may be double-stranded. The adaptor may comprise double-strandedand single-stranded portions. For example, the adaptor may be a Y-shapedadaptor. The adaptor may be a linear adaptor. The adaptor may be acircular adaptor. The adaptor may comprise a molecular barcode, sampleindex, primer sequence, linker sequence or a combination thereof. Themolecular barcode may be adjacent to the sample index. The molecularbarcode may be adjacent to the primer sequence. The sample index may beadjacent to the primer sequence. A linker sequence may connect themolecular barcode to the sample index. A linker sequence may connect themolecular barcode to the primer sequence. A linker sequence may connectthe sample index to the primer sequence.

The adaptor may comprise a molecular barcode. The molecular barcode maycomprise a random sequence. The molecular barcode may comprise apredetermined sequence. Two or more adaptors may comprise two or moredifferent molecular barcodes. The molecular barcodes may be optimized tominimize dimerization. The molecular barcodes may be optimized to enableidentification even with amplification or sequencing errors. Forexamples, amplification of a first molecular barcode may introduce asingle base error. The first molecular barcode may comprise greater thana single base difference from the other molecular barcodes. Thus, thefirst molecular barcode with the single base error may still beidentified as the first molecular barcode. The molecular barcode maycomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. Themolecular barcode may comprise at least 3 nucleotides. The molecularbarcode may comprise at least 4 nucleotides. The molecular barcode maycomprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecularbarcode may comprise less than 10 nucleotides. The molecular barcode maycomprise less than 8 nucleotides. The molecular barcode may compriseless than 6 nucleotides. The molecular barcode may comprise 2 to 15nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. Themolecular barcode may comprise 3 to 10 nucleotides. The molecularbarcode may comprise 3 to 8 nucleotides. The molecular barcode maycomprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6nucleotides.

The adaptor may comprise a sample index. The sample index may comprise arandom sequence. The sample index may comprise a predetermined sequence.Two or more sets of adaptors may comprise two or more different sampleindexes. Adaptors within a set of adaptors may comprise identical sampleindexes. The sample indexes may be optimized to minimize dimerization.The sample indexes may be optimized to enable identification even withamplification or sequencing errors. For examples, amplification of afirst sample index may introduce a single base error. The first sampleindex may comprise greater than a single base difference from the othersample indexes. Thus, the first sample index with the single base errormay still be identified as the first molecular barcode. The sample indexmay comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides.The sample index may comprise at least 3 nucleotides. The sample indexmay comprise at least 4 nucleotides. The sample index may comprise lessthan 20, 19, 18, 17, 16, or 15 nucleotides. The sample index maycomprise less than 10 nucleotides. The sample index may comprise lessthan 8 nucleotides. The sample index may comprise less than 6nucleotides. The sample index may comprise 2 to 15 nucleotides. Thesample index may comprise 2 to 12 nucleotides. The sample index maycomprise 3 to 10 nucleotides. The sample index may comprise 3 to 8nucleotides. The sample index may comprise 4 to 8 nucleotides. Thesample index may comprise 4 to 6 nucleotides.

The adaptor may comprise a primer sequence. The primer sequence may be aPCR primer sequence. The primer sequence may be a sequencing primer.

Adaptors may be attached to one end of a nucleic acid from a sample. Thenucleic acids may be DNA. The DNA may be cell-free DNA (cfDNA). The DNAmay be circulating tumor DNA (ctDNA). The nucleic acids may be RNA.Adaptors may be attached to both ends of the nucleic acid. Adaptors maybe attached to one or more ends of a single-stranded nucleic acid.Adaptors may be attached to one or more ends of a double-strandednucleic acid.

Adaptors may be attached to the nucleic acid by ligation. Ligation maybe blunt end ligation. Ligation may be sticky end ligation. Adaptors maybe attached to the nucleic acid by primer extension. Adaptors may beattached to the nucleic acid by reverse transcription. Adaptors may beattached to the nucleic acids by hybridization. Adaptors may comprise asequence that is at least partially complementary to the nucleic acid.Alternatively, in some instances, adaptors do not comprise a sequencethat is complementary to the nucleic acid.

The method may further comprise fragmenting the cfDNA. The method mayfurther comprise end-repairing the cfDNA. The method may furthercomprise A-tailing the cfDNA.

Further disclosed herein are methods of determining a statisticalsignificance of a selector set. The method may comprise (a) detecting apresence of one or more mutations in one or more samples from a subject,wherein the one or more mutations may be based on a selector setcomprising genomic regions comprising the one or more mutations; (b)determining a mutation type of the one or more mutations present in thesample; and (c) determining a statistical significance of the selectorset by calculating a ctDNA detection index based on a p-value of themutation type of mutations present in the one or more samples.

In some instances, if a rearrangement is observed in two or more samplesfrom the subject, then the ctDNA detection index is 0. At least one ofthe two or more samples may be a plasma sample. At least one of the twoor more samples may be a tumor sample. The rearrangement may be a fusionor a breakpoint.

In some instances, if one type of mutation is present, then the ctDNAdetection index is the p-value of the one type of mutation.

In some instances, if (i) two or more types of mutations are present inthe sample; (ii) the p-values of the two or more types mutations areless than 0.1; and (iii) a rearrangement is not one of the types ofmutations, then the ctDNA detection is calculated based on the combinedp-values of the two or more mutations. The p-values of the two or moremutations may be combined according to Fisher's method. One of the twoor more types of mutations may be a SNV. The p-value of the SNV may bedetermined by Monte Carlo sampling. One of the two or more types ofmutations may be an indel.

In some instances, if (i) two or more types of mutations are present inthe sample; (ii) a p-value of at least one of the two or more types ofmutations are greater than 0.1; and (iii) a rearrangement is not one ofthe types of mutations, then the ctDNA detection is calculated based onthe p-value of one of the two or more types mutations. One of the two ormore types of mutations may be a SNV. The ctDNA detection index may becalculated based on the p-value of the SNV. One of the two or more typesof mutations may be an indel.

Further disclosed herein are methods of identifying rearrangements inone or more nucleic acids. The method may comprise (a) obtainingsequencing information pertaining to a plurality of genomic regions; (b)producing a list of genomic regions, wherein the genomic regions may beadjacent to one or more candidate rearrangement sites or the genomicregions may comprise one or more candidate rearrangement sites; and (c)applying an algorithm to the list of genomic regions to validatecandidate rearrangement sites, thereby identifying rearrangements.

The sequencing information may comprise an alignment file. The alignmentfile may comprise an alignment file of pair-end reads, exon coordinates,and a reference genome.

The sequencing information may be obtained from a database. The databasemay comprise sequencing information pertaining to a population ofsubjects suffering from a disease or condition. The disease or conditionmay be a cancer.

The sequencing information may be obtained from one or more samples fromone or more subjects.

Producing the list of genomic regions may comprise identifyingdiscordant read pairs based on the sequencing information. Thediscordant read-pair may refer to a read and its mate, where: (i) theinsert size may be not equal to the expected distribution of thedataset; or (ii) the mapping orientation of the reads may be unexpected.

Producing the list of genomic regions may comprise classifying thediscordant read pairs based on the sequencing information. Producing thelist of genomic regions further may comprise ranking the genomicregions. The genomic regions may be ranked in decreasing order ofdiscordant read depth.

Producing the list of genomic regions may comprise selecting genomicregions with a minimum user-defined read depth.

The minimum user-defined read depth may be at least 2×, 3×, 4×, 5×, 6×,7×, 8×, 9×, 10× or more.

The method may further comprise eliminating duplicate fragments.

Producing the list of genomic regions may comprise use of one or morealgorithms. The algorithm may analyze properly paired reads in which oneof the paired reads may be truncated to produce a soft-clipped read. Thealgorithm may analyze the soft-clipped reads based on a pattern. Thepattern may be based on x number of skipped bases (Sx) and on y numberof contiguous mapped bases (My). The pattern may be MySx or SxMy.

Applying the algorithm to validate the candidate rearrangement sites maycomprise deleting candidate rearrangements with a read frequency of lessthan 2. Applying the algorithm to validate the candidate rearrangementsites may comprise ranking the candidate rearrangements based on theirread frequency.

Applying the algorithm to validate the candidate rearrangement sites maycomprise comparing two or more reads of the candidate rearrangement.Applying the algorithm to validate the candidate rearrangement sites maycomprise identifying the candidate rearrangement as a rearrangement ifthe two or more reads have a sequence alignment.

Applying the algorithm to validate the candidate rearrangement sites maycomprise evaluating inter-read concordance. Evaluating inter-readconcordance may comprise dividing a first sequencing read of thecandidate rearrangement site into a plurality of subsequences of lengthl. Evaluating inter-read concordance may comprise dividing a secondsequencing read of the candidate rearrangement site into a plurality ofsubsequences of length l. Evaluating inter-read concordance may comprisecomparing the subsequences of the first sequencing read to thesubsequences of the second sequencing read. The first and secondsequencing reads may be considered concordant if a minimum matchingthreshold may be achieved.

Applying the algorithm to validate the candidate rearrangement sites maycomprise in silico validation of the candidate rearrangement sites. Insilico validation may comprise aligning sequencing reads of thecandidate rearrangement site to a reference rearrangement sequence. Thereference rearrangement sequence may be obtained from a referencegenome. The candidate rearrangement site may be identified as arearrangement if the reads map to the reference rearrangement sequencewith an identity of at least 70%, 75%, 80%, 85%, 90%, 95%, 97% or more.

The candidate rearrangement site may be identified as a rearrangement ifthe length of the aligned sequences may be at least 70%, 75%, 80%, 85%,90%, or 95% or more of the read length of the candidate rearrangementsite.

Further disclosed herein are methods of identifying tumor-derived singlenucleotide variations (SNVs). The method may comprise (a) obtaining asample from a subject suffering from a cancer or suspected of sufferingfrom a cancer; (b) conducting a sequencing reaction on the sample toproduce sequencing information; (c) applying an algorithm to thesequencing information to produce a list of candidate tumor allelesbased on the sequencing information from step (b), wherein a candidatetumor allele may comprise a non-dominant base that may be not a germlineSNP; and (d) identifying tumor-derived SNVs based on the list ofcandidate tumor alleles.

Producing the list of candidate tumor alleles may comprise ranking thetumor alleles by their fractional abundance. Producing the list ofcandidate tumor alleles may comprise selecting tumor alleles with afractional abundance in the top 70^(th) 75^(th), 80^(th), 85^(th),87^(th), 90^(th), 92 ^(nd), 95^(th), or 97^(th) percentile. Producingthe list of candidate tumor alleles may comprise selecting tumor alleleswith a fractional abundance of less than 1%, 0.9%, 0.8%, 0.7%, 0.6%,0.5%, 0.4%, 0.3%, 0.2%, 0.1% of the total alleles in the sample from thesubject.

Producing the list of candidate tumor alleles may comprise ranking thetumor alleles based on their sequencing depth. Producing the list ofcandidate tumor alleles may comprise selecting tumor alleles that meet aminimum sequencing depth. The minimum sequencing depth may be at least100×, 200×, 300×, 400×, 500×, 600×, 700×, 800×, 900×, 1000× or more.

Producing the list of candidate tumor alleles may comprise calculating astrand bias percentage of a tumor allele. Producing the list ofcandidate tumor alleles may comprise ranking the tumor alleles based ontheir strand bias percentage. Producing the list of candidate tumoralleles may comprise selecting tumor alleles with a user-defined strandbias percentage. The user-defined strand bias percentage may be lessthan or equal to 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97%.

Producing the list of candidate tumor alleles may comprise comparing thesequence of the tumor allele to a reference tumor allele. Producing thelist of candidate tumor alleles further may comprise identifying tumoralleles that are different from the reference tumor allele.

Identifying the tumor alleles that are different from the referencetumor allele may comprise use of one or more statistical analyses. Theone or more statistical analyses may comprise using Bonferronicorrection to calculate a Bonferroni-adjusted binomial probability forthe tumor allele.

Producing the list of candidate tumor alleles may comprise selectingtumor alleles based on the Bonferroni-adjusted binomial probability. TheBonferroni-adjusted binomial probability of a candidate tumor allele maybe less than or equal to 3×10⁻⁸, 2.9×10⁻⁸, 2.8×10⁻⁸, 2.7×10⁻⁸, 2.6×10⁻⁸,2.5×10⁻⁸, 2.3×10⁻⁸, 2.2×10⁻⁸, 2.1×10⁻⁸, 2.09×10⁻⁸, 2.08×10⁻⁸, 2.07×10⁻⁸,2.06×10⁻⁸, 2.05×10⁻⁸, 2.04×10⁻⁸, 2.03×10⁻⁸, 2.02×10⁻⁸, 2.01×10⁻⁸ or2×10⁻⁸. The Bonferroni-adjusted binomial probability of a candidatetumor allele may be less than or equal to 2.08×10⁻⁸.

Identifying the tumor alleles that are different from the referencetumor allele further may comprise applying a Z-test to theBonferroni-adjusted binomial probability to produce aBonferroni-adjusted single-tailed Z-score for the tumor allele. A tumorallele with a Bonferroni-adjusted single-tailed Z-score of greater thanor equal to 6, 5.9, 5.8, 5.7, 5.6, 5.5, 5.4, 5.3, 5.2, 5.1, or 5.0 maybe considered to be different from the reference tumor allele.

The sample may be a blood sample. The sample may be a paired sample.

Further disclosed herein are methods of producing a selector set. Themethod may comprise (a) obtaining sequencing information of a tumorsample from a subject suffering from a cancer; (b) comparing thesequencing information of the tumor sample to sequencing informationfrom a non-tumor sample from the subject to identify one or moremutations specific to the sequencing information of the tumor sample;and (c) producing a selector set comprising one or more genomic regionscomprising the one or more mutations specific to the sequencinginformation of the tumor sample.

The selector set may comprise sequencing information pertaining to theone or more genomic regions. The selector set may comprise genomiccoordinates pertaining to the one or more genomic regions.

The selector set may be used to produce a plurality of oligonucleotidesthat selectively hybridize the one or more genomic regions. Theplurality of oligonucleotides may be biotinylated.

The one or more mutations may comprise SNVs. The one or more mutationsmay comprise indels. The one or more mutations may compriserearrangements.

Producing the selector set may comprise identifying tumor-derived SNVsusing the methods disclosed herein.

Producing the selector set may comprise identifying tumor-derivedrearrangements using the method disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A-1D: Development of CAncer Personalized Profiling by DeepSequencing (CAPP-Seq). (FIG. 1A) Schematic depicting design of CAPP-Seqselectors and their application for assessing circulating tumor DNA.(FIG. 1B) Multi-phase design of the NSCLC selector. Phase 1: Genomicregions harboring known/suspected driver mutations in NSCLC arecaptured. Phases 2-4: Addition of exons containing recurrent SNVs usingWES data from lung adenocarcinomas and squamous cell carcinomas fromTCGA (n=407). Regions were selected iteratively to maximize the numberof mutations per tumor while minimizing selector size. Recurrenceindex=total unique patients with mutations covered per kb of exon.Phases 5-6: Exons of predicted NSCLC drivers and introns/exons harboringbreakpoints in rearrangements involving ALK, ROS1, and RET were added.Bottom: increase of selector length during each design phase. (FIG. 1C)Analysis of the number of SNVs per lung adenocarcinoma covered by theNSCLC selector in the TCGA WES cohort (Training; n=229) and anindependent lung adenocarcinoma WES data set (Validation; n=183).Results are compared to selectors randomly sampled from the exome(P<1.0×10⁻⁶ for the difference between random selectors and the NSCLCselector). (FIG. 1D) Number of SNVs per patient identified by the NSCLCselector in WES data from three adenocarcinomas from TCGA, colon (COAD),rectal (READ), and endometrioid (UCEC) cancers.

FIG. 2A-2I: Analytical performance. (FIG. 2A-2C) Quality parameters froma representative CAPP-Seq analysis of plasma cfDNA, including lengthdistribution of sequenced cfDNA fragments (FIG. 2A), and depth ofsequencing coverage across all genomic regions in the selector ]FIG.2B). (FIG. 2C) Variation in sequencing depth across cfDNA samples from 4patients. Orange envelope represents s.e.m. (FIG. 2D) Analysis ofbackground rate for 40 plasma cfDNA samples collected from 13 NSCLCpatients and 5 healthy individuals. (FIG. 2E) Analysis of biologicalbackground in d focusing on 107 recurrent somatic mutations from apreviously reported SNaPshot panel. Mutations found in a given patient'stumor were excluded. The mean frequency over all subjects was ˜0.01%. Asingle outlier mutation (TP53 R175H) is indicated by an orange diamond.(FIG. 2F) Individual mutations from e ranked by most to least recurrent,according to mean frequency across the 40 cfDNA samples. The p-valuethreshold of 0.01 (horizontal line) corresponds to the 99^(th)percentile of global selector background in d. (FIG. 2G) Dilution seriesanalysis of expected versus observed frequencies of mutant alleles usingCAPP-Seq. Dilution series were generated by spiking fragmented HCC78 DNAinto control cfDNA. (FIG. 2H) Analysis of the effect of the number ofSNVs considered on the estimates of fractional abundance (95% confidenceintervals shown in gray). (FIG. 2I) Analysis of the effect of the numberof SNVs considered on the mean correlation coefficient between expectedand observed cancer fractions (blue dashed line) using data from panelh. 95% confidence intervals are shown for e-f. Statistical variation forg is shown as s.e.m.

FIG. 3A-3C: Sensitivity and specificity analysis. (FIG. 3A) ReceiverOperating

Characteristic (ROC) analysis of cfDNA samples from pre-treatmentsamples and healthy controls, divided into all stages (n=13 patients)and stages II-IV (n=9 patients). Area Under the Curve (AUC) values aresignificant at P<0.0001. Sn, sensitivity; Sp, specificity. (FIG. 3B) Rawdata related to a. TP, true positive; FP, false positive; TN, truenegative; FN, false negative. (FIG. 3C) Concordance between tumorvolume, measured by CT or PET/CT, and pg per mL of ctDNA frompretreatment samples (n=9), measured by CAPP-Seq. Patients P6 and P9were excluded due to inability to accurately assess tumor volume anddifferences related to the capture of fusions, respectively. Of note,linear regression was performed in non-log space; the log-log axes anddashed diagonal line are for display purposes only.

FIG. 4A-4I: Noninvasive detection and monitoring of circulating tumorDNA. (FIG. 4A-4H) Disease monitoring using CAPP-Seq. (FIG. 4A-4B)Disease burden changes in response to treatment in a stage III NSCLCpatient using SNVs and an indel (FIG. 4A), and a stage IV NSCLC patientusing three rearrangement breakpoints (FIG. 4B). (FIG. 4C) Concordancebetween different reporters (SNVs and a fusion) in a stage IV NSCLCpatient. (FIG. 4D) Detection of a subclonal EGFR T790M resistancemutation in a patient with stage IV NSCLC. The fractional abundance ofthe dominant clone and T790M-containing clone are shown in the primarytumor (left) and plasma samples (right). (FIG. 4E-4F) CAPP-Seq resultsfrom post-treatment cfDNA samples are predictive of clinical outcomes ina stage IIB NSCLC patient FIG. 4E and Stage IIIB NSCLC patient (FIG.4F). (FIG. 4G-4H) Monitoring of tumor burden following complete tumorresection (FIG. 4G) and Stereotactic Ablative Radiotherapy (SABR) (FIG.4H) for two stage IB NSCLC patients. (FIG. 4I) Exploratory analysis ofthe potential application of CAPP-Seq for biopsy-free tumor genotypingor cancer screening. All plasma cfDNA samples from patients in Table 1were examined for the presence of mutant allele outliers withoutknowledge of the primary tumor mutations; samples with detectablemutations are shown, along with two samples determined to becancer-negative (P1-2 and P16-3) and a sample without tumor-derived SNVs(P9-5; see Table 1). The lowest mutant allele fraction detected was˜0.5% (dashed horizontal line). Error bars in d represent s.e.m. Tu,tumor; Ef, pleural effusion; SD, stable disease; PD, progressivedisease; PR, partial response; CR, complete response; DOD, dead ofdisease.

FIG. 5A-5B: Comparison to other methods for detection of ctDNA inplasma. (FIG. 5A) Analytical modeling of CAPP-Seq, WES, and WGS fordifferent detection limits of tumor cfDNA in plasma. Calculations arebased on the median number of mutations detected per NSCLC for CAPP-Seq(e.g., 4) and the reported number of mutations in NSCLC exomes andgenomes. The vertical dotted line represents the median fraction oftumor-derived cfDNA in plasma from NSCLC patients in this study (seebelow). (FIG. 5B) Costs for WES and WGS to achieve the same theoreticaldetection limit as CAPP-Seq (shown as a dark solid line in FIG. 5A).

FIG. 6: CAPP-Seq computational pipeline. Major steps of thebioinformatics pipeline for mutation discovery and quantitation inplasma are schematically illustrated.

FIG. 7A-7B: Statistical enrichment of recurrently mutated NSCLC exonscaptures known drivers. We employed two metrics to prioritize exons withrecurrent mutations for inclusion in the CAPP-Seq NSCLC selector. Thefirst, termed Recurrence Index (RI), is defined as the number of uniquepatients (e.g. tumors) with somatic mutations per kilobase of a givenexon and the second metric is based on the minimum number of uniquepatients (e.g. tumors) with mutations in a given kb of exon. We analyzedexons containing at least one non-silent SNV genotyped by TCGA(n=47,769) in a combined cohort of 407 lung adenocarcinoma (LUAD) andsquamous cell carcinoma (SCC) patients. (FIG. 7A Known/suspected NSCLCdrivers are highly enriched at RI≧30 (inset), comprising 1.8% (n=861) ofanalyzed exons. (FIG. 7B) Known/suspected NSCLC drivers are highlyenriched at ≧3 patients with mutations per exon (inset), encompassing16% of analyzed exons.

FIG. 8A-8E: FACTERA analytical pipeline for breakpoint mapping. Majorsteps used by FACTERA to precisely identify genomic breakpoints fromaligned paired-end sequencing data are anecdotally illustrated using twohypothetical genes, w and v. (FIG. 8A) Improperly paired, or“discordant,” reads (indicated in yellow) are used to locate genesinvolved in a potential fusion (in this case, w and v). (FIG. 8B)Because truncated (e.g., soft-clipped) reads may indicate a fusionbreakpoint, any such reads within genomic regions delineated by w and vare also further analyzed. (FIG. 8C) Consider soft-clipped reads, R1 andR2, whose non-clipped segments map to w and v, respectively. If R1 andR2 derive from a fragment encompassing a true fusion between w and v,then the mapped portion of R1 should match the soft-clipped portion ofR2, and vice versa. This is assessed by FACTERA using fast k-merindexing and comparison. (FIG. 8D) Four possible orientations of R1 andR2 are depicted. However, only Cases 1a and 2a can generate validfusions. Thus, prior to k-mer comparison (FIG. 8C), the reversecomplement of R1 is taken for Cases 1b and 2b, respectively, convertingthem into Cases 1a and 2a. (FIG. 8E) In some cases, short sequencesimmediately flanking the breakpoint are identical, preventingunambiguous determination of the breakpoint. Let iterators i and jdenote the first matching sequence positions between R1 and R2. Toreconcile sequence overlap, FACTERA arbitrarily adjusts the breakpointin R2 (e.g., bp2) to match R1 (e.g., bp1) using the sequence offsetdetermined by differences in distance between bp2 and i, and bp1 and j.Two cases are illustrated, corresponding to sequence orientationsdescribed in FIG. 8D.

FIG. 9A-9B: Application of FACTERA to NSCLC cell lines NCI-H3122 andHCC78, and Sanger-validation of breakpoints. (FIG. 9A) Pile-up of asubset of soft-clipped reads mapping to the EML4-ALK fusion identifiedin NCI-H3122 along with the corresponding Sanger chromatogram (from topto bottom SEQ ID NOs:1-11). (FIG. 9B) Same as a, but for theSLC34A2-ROS1 translocation identified in HCC78 (from top to bottom SEQID NOs:12-22).

FIG. 10A-10C: Improvements in CAPP-Seq performance with optimizedlibrary preparation procedures. Using 32 ng of input cfDNA from plasma,we compared standard versus ‘with bead’⁵ library preparation methods, aswell as two commercially available DNA polymerases (Phusion and KAPAHiFi). We also compared template pre-amplification by Whole GenomeAmplification (WGA) using Degenerate Oligonucleotide PCR (DOP). Indicesconsidered for these comparisons included (FIG. 10A) length of thecaptured cfDNA fragments sequenced, (FIG. 10B) depth and uniformity ofsequencing coverage across all genomic regions in the selector, and(FIG. 10C) sequence mapping and capture statistics, includinguniqueness. Collectively, these comparisons identified KAPA HiFipolymerase and a “with bead” protocol as having most robust and uniformperformance.

FIG. 11A-11F: Optimizing allele recovery from low input cfDNA duringIllumina library preparation. Bars reflect the relative yield ofCAPP-Seq libraries constructed from 4 ng cfDNA, calculated by averagingquantitative PCR measurements of n=4 pre-selected reporters withinCAPP-Seq with pre-defined amplification efficiencies. (FIG. 11A) Sixteenhour ligation at 16° C. increases ligation efficiency and reporterrecovery. (FIG. 11B) Adapter ligation volume did not have a significanteffect on ligation efficiency and reporter recovery. (FIG. 11C)Performing enzymatic reactions “with-bead” to minimize tube transfersteps increases reporter recovery. (FIG. 11D) Increasing adapterconcentration during ligation increases ligation efficiency and reporterrecovery. Reporter recovery is also higher when using KAPA HiFi DNApolymerase compared to Phusion DNA polymerase (FIG. 11E) and when usingthe KAPA Library Preparation Kit with the modifications in a-d comparedto the NuGEN SP Ovation Ultralow Library System with automation on aMondrian SP Workstation (FIG. 11F). Relative reporter abundance wasdetermined by qPCR using the 2^(−ΔCt) method. A two-sided t test withequal variance was used to test the statistical significance betweengroups. All values are presented as means±s.d. N.S., not significant.Based on these results, we estimate that combining the methodologicalmodifications in FIG. 11A and FIG. 11C-11E improves yield in NGSlibraries by 3.3-fold.

FIG. 12A-12C: CAPP-Seq performance with various amounts of input cfDNA.(FIG. 12A) Length of the captured cfDNA fragments sequenced. (FIG. 12B)Depth of sequencing coverage across all genomic regions in the selector(pre-duplicate removal). (FIG. 12C) Sequence mapping and capturestatistics. As expected, more input cfDNA mass correlates with moreunique fragments sequenced.

FIG. 13A-13B. Analysis of library complexity and molecule recovery.(FIG. 13A) The expected proportion of additional library complexitypresent in post-duplicate reads is plotted for all patient and controlsamples, including plasma cfDNA (n=40) and paired tumor/PBL specimens(n=17 each). Because of the highly stereotyped size of cfDNA fragmentsoccurring naturally in blood plasma, when compared with genomic DNAshorn by sonication, any two fragments of DNA circulating in plasma areinherently more likely by chance to have arisen from different originalmolecules, whether considering tumor or non-tumor cells as the source ofthis cfDNA. To estimate this “missing” complexity, we reasoned that twoDNA fragments (e.g., paired end reads) with identical start/endcoordinates that differ by a single a priori defined germline variant(e.g. one maternal and one paternal allele) represent two unique andindependent starting molecules rather than technical artifacts (e.g. PCRduplicates). Therefore, the number of fragments sharing identicalstart/end coordinates with both maternal and paternal germline allelesof heterozygous SNPs were used to estimate additional librarycomplexity. Library complexity estimates updated to factor in these dataare also provided in Tables 3, 20 and 21 and determined as describedherein. (FIG. 13B) Empirical assessment of molecule recovery in cfDNA(n=40) by determination of the mass of DNA produced compared to theexpected library yield based on mass input, number of PCR cycles, andefficiency (mean=46%). (FIG. 13A-13B) Values are presented as means±95%confidence intervals.

FIG. 14. Analysis of library cross-contamination. Allelic fractions ofpatient-specific homozygous germline SNPs were assessed in cfDNA samplesmultiplexed on the same lane. SNPs were called as described in theMethods. The mean “cross-contamination” rate in cfDNA samples was 0.06%,shown by the horizontal dotted line. This level of contamination is toolow to affect our estimates of tumor burden given the low fraction oftumor-derived cfDNA in plasma of NSCLC patients (median of ˜0.1%; FIG. 5a) (e.g., 0.06×0.1=0.006% of a given sample would on average representcontamination from ctDNA of another sample). Of note, to minimize therisk of inter-sample contamination, we use aerosol barrier tips, work inhoods, and do not multiplex tumor and plasma libraries in the same lane.

FIG. 15. Analysis of selector-wide bias in captured sequence. Becausethe NSCLC selector was designed to target the hg19 reference genome, wereasoned that selector bias for SNVs, if any, should be discernable as asystematically lower ratio of non-reference to reference alleles inheterozygous germline SNPs. Therefore, we analyzed high confidence SNPsdetected by VarScan in patient PBL samples, where high confidence wasdefined as variants with a non-reference fraction >10% present in thecommon SNPs subset of dbSNP (version 137.0). As shown, we detected avery small skew toward reference (8 of 11 samples have a mediannon-reference allelic frequency of 49%; the remaining 3 samples areunbiased). Importantly, such bias appears too small to significantlyaffect our results. Boxes represent the interquartile range, andwhiskers encapsulate the 10^(th) to 90^(th) percentiles. Germline SNPswere identified using VarScan 2.

FIG. 16A-16D: Empirical spiking analysis of CAPP-Seq using two NSCLCcell lines. (FIG. 16A) Expected and observed (by CAPP-Seq) fractions ofNCI-H3122 DNA spiked into control HCC78 DNA are linear for all fractionstested (0.1%, 1%, and 10%; R²=1). Using data from FIG. 16B, analysis ofthe effect of the number of SNVs considered on the estimates offractional abundance (95% confidence intervals shown in gray). (FIG.16C) Analysis of the effect of the number of SNVs considered on the meancorrelation coefficient and coefficient of variation between expectedand observed cancer fractions (blue dashed line) using data from panela. (FIG. 16D) Expected and observed fractions of the EML4-ALK fusionpresent in HCC78 are linear (R²=0.995) over all spiking concentrationstested (see FIG. 9B for breakpoint verification). The observed EML4-ALKfractions were normalized based on the relative abundance of the fusionin 100% H3122 DNA. Moreover, both a single heterozygous insertion(‘Indel’; chr7: 107416855, +T) and a 4.9 kb homozygous deletion(‘Deletion’, chr17: 29422259-29592392) in NCI-H3122 were concordant withdefined concentrations. Values in a are presented as means±s.e.m.

FIG. 17A-17B: Base-pair resolution breakpoint mapping for all patientsand cell lines enumerated by FACTERA. Gene fusions involving ALK (FIG.17A) and ROS1 (FIG. 17B) are graphically depicted. Schematics in the toppanels indicate the exact genomic positions (HG19 NCBI Build37.1/GRCh37) of the breakpoints in ALK, ROS1, EML4, KIF5B, SLC34A2,CD74, MKX, and FYN. Bottom panels depict exons flanking the predictedgene fusions with notation indicating the 5′ fusion partner gene andlast fused exon followed by the 3′ fusion partner gene and first fusedexon. For example, in S13del37;R34 exons 1-13 of SLC34A2 (excluding the3′ 37 nucleotides of exon 13) are fused to exons 34-43 of ROS1. Exons inFYN are from its 5′UTR and precede the first coding exon. The greendotted line in the predicted FYN-ROS1 fusion indicates the firstin-frame methionine in ROS1 exon 33, which preserves an open readingframe encoding the ROS1 kinase domain. All rearrangements were eachindependently confirmed by PCR and/or FISH.

FIG. 18: Presence of fusions is inversely related to the number of SNVsdetected by CAPP-Seq. For each patient listed in Table 1 the number ofidentified SNVs versus the presence (n=11) or absence (n=6) of detectedgenomic fusions is plotted. Statistical significance was determinedusing a two-sided Wilcoxon rank sum test, and summarized values arepresented as means±s.e.m.

FIG. 19A-19D. Receiver Operating Curve (ROC) analysis of CAPP-Seqperformance including both pre- and post-treatment samples. Comparisonof sensitivity and specificity achieved for non-deduped (FIGS. 19A and19C) and deduped (post PCR duplicate removal) data (FIGS. 19B and 19D).In addition, all stages (FIG. 19A-19B) are compared with intermediate toadvanced stages (stages II-IV, FIGS. 19C and 19D). Finally, for all ROCanalyses, the effect of the indel/fusion filter onsensitivity/specificity is shown. Reporter fractions for bothnon-deduped and deduped cfDNA samples are provided in Table 4.

FIG. 20. CAPP-Seq sensitivity and specificity over all patient reportersand sequenced plasma cfDNA samples. All values shown reflect a ctDNAdetection index of 0.03. See Methods for details on detection metrics,and determination of cancer-positive, cancer-negative, and unknowncategories.

FIG. 21A-21D. Non-invasive cancer screening with CAPP-Seq, related toFIG. 4I. (FIG. 21A) Steps to identify candidate SNVs in plasma cfDNAdemonstrated using a patient sample with NSCLC (P6, see Table 4).Following stepwise filtration, outlier detection is applied. (FIG. 21B)Same as a, but using a plasma cfDNA sample from a patient who had theirtumor surgically removed. No SNVs are identified, as expected. (FIG.21C, 21D) Three additional representative samples applying retrospectivescreening to patients analyzed in this study. P2 and P5 samples haveconfirmed tumor-derived SNVs, while P9 is cancer positive but lackstumor-derived SNVs. Red points, confirmed tumor-derived SNVs; Greenpoints, background noise.

FIG. 22. depicts a flow chart of patient analysis.

FIG. 23. shows a system for implementing the methods of the disclosure.

DETAILED DESCRIPTION OF THE INVENTION

It is characteristic of cancer cells that due to somatic mutation thegenome sequence of the cancer cell is changed from the genome sequenceof the individual from which it is derived. Most human cancers arerelatively heterogeneous for somatic mutations in individual genes.Specifically, in most human tumors, recurrent somatic alterations ofsingle genes account for a minority of patients, and only a minority oftumor types can be defined using a small number of recurrent mutationsat predefined positions. The present invention solves this problem byuse of enrichment of tumor-derived nucleic acid molecules from totalgenomic nucleic acids with a selector set. The design of the selector isvital because (1) it dictates which mutations can be detected in withhigh probability for a patient with a given cancer, and (2) the selectorsize (in kb) directly impacts the cost and depth of sequence coverage.

While the specific genetic changes differ from individual to individualand between types of cancer, there are regions of the genome that showrecurrent changes. In those regions there is an increased probabilitythat any given individual cancer will show genetic variation. Thegenetic changes in cancer cells provide a means by which cancer cellscan be distinguished from normal (e.g., non-cancer) cells. Cell-freeDNA, for example the DNA fragments found in blood samples, can beanalyzed for the presence of genetic variation distinctive of tumorcells. However, the absolute levels of tumor DNA in such samples isoften small, and the genetic variation may represent only a very smallportion of the entire genome. The present invention addresses this issueby providing methods for selective detection of mutated regionsassociated with cancer, thereby allowing accurate detection of cancercell DNA or RNA from the background of normal cell DNA or RNA. Althoughthe methods disclosed herein may specifically refer to DNA (e.g.,cell-free DNA, circulating tumor DNA), it should be understood that themethods, compositions, and systems disclosed herein are applicable toall types of nucleic acids (e.g., RNA, DNA, RNA/DNA hybrids).

Provided herein are methods for the ultrasensitive detection of aminority nucleic acid in a heterogeneous sample. The method may comprise(a) obtaining sequence information of a cell-free DNA (cfDNA) samplederived from a subject; and (b) using sequence information derived from(a) to detect cell-free minority nucleic acids in the sample, whereinthe method is capable of detecting a percentage of the cell-freeminority nucleic acids that is less than 2% of total cfDNA. The minoritynucleic acid may refer to a nucleic acid that originated from a cell ortissue that is different from a normal cell or tissue from the subject.For example, the subject may be infected with a pathogen such as abacteria and the minority nucleic acid may be a nucleic acid from thepathogen. In another example, the subject is a recipient of a cell,tissue or organ from a donor and the minority nucleic acid may be anucleic acid originating from the cell, tissue or organ from the donor.In another example, the subject is a pregnant subject and the minoritynucleic acid may be a nucleic acid originating from a fetus. The methodmay comprise using the sequence information to detect one or moresomatic mutations in the fetus. The method may comprise using thesequence information to detect one or more post-zygotic mutations in thefetus. Alternatively, the subject may be suffering from a cancer and theminority nucleic acid may be a nucleic acid originating from a cancercell.

Provided herein are methods for the ultrasensitive detection ofcirculating tumor DNA in a sample. The method may be called CAncerPersonalized Profiling by Deep Sequencing (CAPP-Seq). The method maycomprise (a) obtaining sequence information of a cell-free DNA (cfDNA)sample derived from a subject; and (b) using sequence informationderived from (a) to detect cell-free tumor DNA (ctDNA) in the sample,wherein the method is capable of detecting a percentage of ctDNA that isless than 2% of total cfDNA. CAPP-Seq may accurately quantify cell-freetumor DNA from early and advanced stage tumors. CAPP-Seq may identifymutant alleles down to 0.025% with a detection limit of <0.01%.Tumor-derived DNA levels often paralleled clinical responses to diversetherapies and CAPP-Seq may identify actionable mutations. CAPP-Seq maybe routinely applied to noninvasively detect and monitor tumors, thusfacilitating personalized cancer therapy.

Disclosed herein are methods for determining a quantity of circulatingtumor DNA (ctDNA) in a sample. The method may comprise (a) ligating oneor more adaptors to cell-free DNA (cfDNA) derived from a sample from asubject to produce one or more adaptor-ligated cfDNA; (b) performingsequencing on the one or more adaptor-ligated cfDNA, wherein theadaptor-ligated cfDNA to be sequenced is based on a selector setcomprising a plurality of genomic regions; and (c) using a computerreadable medium to determine a quantity of cfDNA originating from atumor based on the sequencing information obtained from theadaptor-ligated cfDNA.

Further disclosed herein are methods of detecting, diagnosing, orprognosing a status or outcome of a cancer in a subject. The method maycomprise (a) obtaining sequence information of a cell-free DNA (cfDNA)sample derived from the subject; (b) using sequence information derivedfrom (a) to detect cell-free tumor DNA (ctDNA) in the sample wherein themethod is capable of detecting a percentage of ctDNA that is less than2% of total cfDNA.

Further disclosed herein are methods of diagnosing a status or outcomeof a cancer in a subject. The method may comprise (a) obtaining sequenceinformation of cell-free genomic DNA derived from a sample from asubject, wherein the sequence information is derived from genomicregions that are mutated in at least 80% of a population of subjectsafflicted with a cancer; and (b) diagnosing a cancer selected from agroup consisting of lung cancer, breast cancer, colorectal cancer andprostate cancer in the subject based on the sequence information,wherein the method has a sensitivity of 80%.

Further disclosed herein are methods of prognosing a status or outcomeof a cancer in a subject. The method may comprise (a) obtaining sequenceinformation of cell-free genomic DNA derived from a sample from asubject, wherein the sequence information is derived from regions thatare mutated in at least 80% of a population of subjects afflicted with acondition; and (b) determining a prognosis of a condition in the subjectbased on the sequence information.

Further disclosed herein are methods of selecting a therapy for asubject suffering from a cancer. The method may comprise (a) obtainingsequence information of a cell-free DNA (cfDNA) sample derived from thesubject; (b) using sequence information derived from (a) to detectcell-free tumor DNA (ctDNA) in the sample wherein the method is capableof detecting a percentage of ctDNA that is less than 2% of total cfDNA.

Alternatively, the method may comprise (a) obtaining sequenceinformation of cell-free genomic DNA derived from a sample from asubject, wherein the sequence information is derived from regions thatare mutated in at least 80% of a population of subjects afflicted with acondition; and (b) determining a therapeutic regimen of a condition inthe subject based on the sequence information.

Further disclosed herein are methods for diagnosing, prognosing, ordetermining a therapeutic regimen for a subject afflicted with orsuspected of having a cancer. The method may comprise (a) obtainingsequence information for selected regions of genomic DNA from acell-free DNA sample from the subject; (b) using the sequenceinformation to determine the presence or absence of one or moremutations in the selected regions, wherein at least 70% of a populationof subjects afflicted with the cancer have mutation(s) in the regions;and (c) providing a report with a diagnosis, prognosis or treatmentregimen to the subject, based on the presence or absence of the one ormore mutations.

Further disclosed herein are methods for assessing tumor burden in asubject. The method may comprise (a) obtaining sequence information oncell-free nucleic acids derived from a sample from the subject; (b)using a computer readable medium to determine quantities of circulatingtumor DNA (ctDNA) in the sample; (c) assessing tumor burden based on thequantities of ctDNA; and (d) reporting the tumor burden to the subjector a representative of the subject.

Further disclosed herein are methods for determining a disease state ofa cancer in a subject. The method may comprise (a) obtaining a quantityof circulating tumor DNA (ctDNA) in a sample from the subject; (b)obtaining a volume of a tumor in the subject; and (c) determining adisease state of a cancer in the subject based on a ratio of thequantity of ctDNA to the volume of the tumor.

Disclosed herein are methods for detecting at least 50% of stage Icancer with a specificity of greater than 90%. The method may comprise(a) performing sequencing on cell-free DNA derived from a sample,wherein the cell-free DNA to be sequenced is based on a selector setcomprising a plurality of genomic regions; (b) using a computer readablemedium to determine a quantity of the cell-free DNA based on thesequencing information of the cell-free DNA; and (c) detecting a stage Icancer in the sample based on the quantity of the cell-free DNA.

Disclosed herein are methods for detecting at least 60% of stage IIcancer with a specificity of greater than 90% comprising (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced is based on a selector set comprising a plurality ofgenomic regions; (b) using a computer readable medium to determine aquantity of the cell-free DNA based on the sequencing information of thecell-free DNA; and (c) detecting a stage II cancer in the sample basedon the quantity of the cell-free DNA.

Disclosed herein are methods for detecting at least 60% of stage IIIcancer with a specificity of greater than 90% comprising (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced is based on a selector set comprising a plurality ofgenomic regions; (b) using a computer readable medium to determine aquantity of the cell-free DNA based on the sequencing information of thecell-free DNA; and (c) detecting a stage III cancer in the sample basedon the quantity of the cell-free DNA.

Disclosed herein are methods for detecting at least 60% of stage IVcancer with a specificity of greater than 90% comprising (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced is based on a selector set comprising a plurality ofgenomic regions; (b) using a computer readable medium to determine aquantity of the cell-free DNA based on the sequencing information of thecell-free DNA; and (c) detecting a stage IV cancer in the sample basedon the quantity of the cell-free DNA.

Also provided are selector sets for use in the methods disclosed herein.The selector set may comprise a plurality of genomic regions comprisingone or more mutations present in a population of subjects suffering froma cancer. The selector set may be a library of recurrently mutatedgenomic regions used in the CAPP-Seq methods. The targeting ofrecurrently mutated genomic regions may allow a distinction betweentumor cell DNA and normal DNA. In addition, the targeting of recurrentlymutated genomic region may provide for simultaneous detection of pointmutations, copy number variation, insertions/deletions, andrearrangements.

The selector set may be a computer readable medium. The computerreadable medium may comprise nucleic acid sequence information for twoor more genomic DNA regions wherein (a) the genomic regions comprise oneor more mutations in >80% of tumors from a population of subjectsafflicted with a cancer; (b) the genomic DNA regions represent less than1.5 Mb of the genome; and (c) one or more of the following: (i) thecondition is not hairy cell leukemia, ovarian cancer, Waldenstrom'smacroglobulinemia; (ii) each of the genomic DNA regions comprises atleast one mutation in at least one subject afflicted with the cancer;(iii) the cancer includes two or more different types of cancer; (iv)the two or more genomic regions are derived from two or more differentgenes; (v) the genomic regions comprise two or more mutations; or (vi)the two or more genomic regions comprise at least 10 kb.

The selector set may provide, for example, oligonucleotides useful inselective amplification of tumor-derived nucleic acids. The selector setmay provide, for example, oligonucleotides useful in selective captureor enrichment of tumor-derived nucleic acids. Disclosed herein arecompositions comprising a set of oligonucleotides based on the selectorset. The composition may comprise a set of oligonucleotides thatselectively hybridize to a plurality of genomic DNA regions, wherein(a) >80% of tumors from a population of cancer subjects include one ormore mutations in the genomic DNA regions; (b) the plurality of genomicDNA regions represent less than 1.5 Mb of the genome; and (c) the set ofoligonucleotides comprise 5 or more different oligonucleotides thatselectively hybridize to the plurality of genomic DNA regions.

The composition may comprise oligonucleotides that selectively hybridizeto a plurality of genomic regions, wherein the genomic regions comprisea plurality of mutations present in >60% of a population of subjectssuffering from a cancer.

Further disclosed herein is an array comprising a plurality ofoligonucleotides to selectively capture genomic regions, wherein thegenomic regions comprise a plurality of mutations present in >60% of apopulation of subjects suffering from a cancer.

Further disclosed herein are methods of producing a selector set for acancer. The method of producing a selector set for a cancer may comprise(a) identifying recurrently mutated genomic DNA regions of the selectedcancer; and (b) prioritizing regions using one or more of the followingcriteria (i) a Recurrence Index (RI) for the genomic region(s), whereinthe RI is the number of unique patients or tumors with somatic mutationsper length of a genomic region; and (ii) a minimum number of uniquepatients or tumors with mutations in a length of genomic region.

Disclosed herein are methods of enriching for circulating tumor DNA froma sample.

The method may comprise contacting cell-free nucleic acids from a samplewith a plurality of oligonucleotides, wherein the plurality ofoligonucleotides selectively hybridize to a plurality of genomic regionscomprising a plurality of mutations present in >60% of a population ofsubjects suffering from a cancer.

Alternatively, the method may comprise contacting cell-free nucleicacids from a sample with a set of oligonucleotides, wherein the set ofoligonucleotides selectively hybridize to a plurality of genomicregions, wherein (a) >80% of tumors from a population of cancer subjectsinclude one or more mutations in the genomic regions; (b) the pluralityof genomic regions represent less than 1.5 Mb of the genome; and (c) theset of oligonucleotides comprise 5 or more different oligonucleotidesthat selectively hybridize to the plurality of genomic regions.

Further disclosed herein are methods of preparing a nucleic acid samplefor sequencing.

The method may comprise (a) conducting an amplification reaction oncell-free DNA (cfDNA) derived from a sample to produce a plurality ofamplicons, wherein the amplification reaction comprises 20 or feweramplification cycles; and (b) producing a library for sequencing, thelibrary comprising the plurality of amplicons.

Further disclosed herein are systems for implementing one or more of themethods or steps of the methods disclosed herein. FIG. 23 shows acomputer system (also “system” herein) 2301 programmed or otherwiseconfigured for implementing the methods of the disclosure, such asproducing a selector set and/or data analysis. The system 2301 includesa central processing unit (CPU, also “processor” and “computerprocessor” herein) 2305, which can be a single core or multi coreprocessor, or a plurality of processors for parallel processing. Thesystem 2301 also includes memory 2310 (e.g., random-access memory,read-only memory, flash memory), electronic storage unit 2315 (e.g.,hard disk), communications interface 2320 (e.g., network adapter) forcommunicating with one or more other systems, and peripheral devices2325, such as cache, other memory, data storage and/or electronicdisplay adapters. The memory 2310, storage unit 2315, interface 2320 andperipheral devices 2325 are in communication with the CPU 2305 through acommunications bus (solid lines), such as a motherboard. The storageunit 2315 can be a data storage unit (or data repository) for storingdata. The system 2301 is operatively coupled to a computer network(“network”) 2330 with the aid of the communications interface 2320. Thenetwork 2330 can be the Internet, an internet and/or extranet, or anintranet and/or extranet that is in communication with the Internet. Thenetwork 2330 in some cases is a telecommunication and/or data network.The network 2330 can include one or more computer servers, which canenable distributed computing, such as cloud computing. The network 2330in some cases, with the aid of the system 2301, can implement apeer-to-peer network, which may enable devices coupled to the system2301 to behave as a client or a server.

The system 2301 is in communication with a processing system 2335. Theprocessing system 2335 can be configured to implement the methodsdisclosed herein. In some examples, the processing system 2335 is anucleic acid sequencing system, such as, for example, a next generationsequencing system (e.g., Illumina sequencer, Ion Torrent sequencer,Pacific Biosciences sequencer). The processing system 2335 can be incommunication with the system 2301 through the network 2330, or bydirect (e.g., wired, wireless) connection. The processing system 2335can be configured for analysis, such as nucleic acid sequence analysis.

Methods as described herein can be implemented by way of machine (orcomputer processor) executable code (or software) stored on anelectronic storage location of the system 2301, such as, for example, onthe memory 2310 or electronic storage unit 2315. During use, the codecan be executed by the processor 2305. In some examples, the code can beretrieved from the storage unit 2315 and stored on the memory 2310 forready access by the processor 2305. In some situations, the electronicstorage unit 2315 can be precluded, and machine-executable instructionsare stored on memory 2310.

Disclosed herein is a computer-implemented system for calculating arecurrence index for one or more genomic regions. Thecomputer-implemented system may comprise (a) a digital processing devicecomprising an operating system configured to perform executableinstructions and a memory device; and (b) a computer program includinginstructions executable by the digital processing device to create arecurrence index, the computer program comprising (i) a first softwaremodule configured to receive data pertaining to a plurality ofmutations; (ii) a second software module configured to relate theplurality of mutations to one or more genomic regions and/or one or moresubjects; and (iii) a third software module configured to calculate arecurrence index of one or more genomic regions, wherein the recurrenceindex is based on a number of mutations per subject per kilobase ofnucleotide sequence.

Selector Set

The methods, kits, and systems disclosed herein may comprise one or moreselector sets or uses thereof. A selector set may be a bioinformaticsconstruct comprising the sequence information for regions of the genome(e.g., genomic regions) associated with one or more cancers of interest.A selector set may be a bioinformatics construct comprising genomiccoordinates for one or more genomic regions. The genomic regions maycomprise one or more recurrently mutated regions. The genomic regionsmay comprise one or more mutations associated with one or more cancersof interest.

The number of genomic regions in a selector set may vary depending onthe nature of the cancer. The inclusion of larger numbers of genomicregions may generally increase the likelihood that a unique somaticmutation will be identified. Including too many genomic regions in thelibrary is not without a cost, however, since the number of genomicregions is directly related to the length of nucleic acids that must besequenced in the analysis. At the extreme, the entire genome of a tumorsample and a genomic sample could be sequenced, and the resultingsequences could be compared to note any differences.

The selector sets of the invention may address this problem byidentifying genomic regions that are recurrently mutated in a particularcancer, and then ranking those regions to maximize the likelihood thatthe region will include a distinguishing somatic mutation in aparticular tumor. The library of recurrently mutated genomic regions, or“selector set”, can be used across an entire population for a givencancer or class of cancers, and does not need to be optimized for eachsubject.

The selector set may comprise at least about 2, 3, 4, 5, 6, 7, 8, or 9different genomic regions. The selector set may comprise at least about10 different genomic regions; at least about 25, at least about 50, atleast about 100, at least about 150, at least about 200, at least about250, at least about 300, at least about 350, at least about 400, atleast about 500, at least about 600, at least about 700, at least about800, at least about 900, at least about 1000 or more different genomicregions.

The selector set may comprise between about 10 to about 1000 differentgenomic regions. The selector set may comprise between about 10 to about900 different genomic regions. The selector set may comprise betweenabout 10 to about 800 different genomic regions. The selector set maycomprise between about 10 to about 700 different genomic regions. Theselector set may comprise between about 20 to about 600 differentgenomic regions. The selector set may comprise between about 20 to about500 different genomic regions. The selector set may comprise betweenabout 20 to about 400 different genomic regions. The selector set maycomprise between about 50 to about 500 different genomic regions. Theselector set may comprise between about 50 to about 400 differentgenomic regions. The selector set may comprise between about 50 to about300 different genomic regions.

The selector set may comprise a plurality of genomic regions. Theplurality of genomic regions may comprise at most 5000 different genomicregions. In some embodiments, the plurality of genomic regions comprisesat most 2000 different genomic regions. In some embodiments, theplurality of genomic regions comprises at most 1000 different genomicregions. In some embodiments, the plurality of genomic regions comprisesat most 500 different genomic regions. In some embodiments, theplurality of genomic regions comprises at most 400 different genomicregions. In some embodiments, the plurality of genomic regions comprisesat most 300 different genomic regions. In some embodiments, theplurality of genomic regions comprises at most 200 different genomicregions. In some embodiments, the plurality of genomic regions comprisesat most 150 different genomic regions. In some embodiments, theplurality of genomic regions comprises at most 100 different genomicregions. In some embodiments, the plurality of genomic regions comprisesat most 50 different genomic regions or even fewer.

A genomic region may comprise a protein-coding region, or portionthereof. A protein-coding region may refer to a region of the genomethat encodes for a protein. A protein-coding region may comprise anintron, exon, and/or untranslated region (UTR). A genomic region maycomprise two or more protein-coding regions, or portions thereof. Forexample, a genomic region may comprise a portion of an exon and aportion of an intron. A genomic region may comprise three or moreprotein-coding regions, or portions thereof. For example, a genomicregion may comprise a portion of a first exon, a portion of an intron,and a portion of a second exon. Alternatively, or additionally, agenomic region may comprise a portion of an exon, a portion of anintron, and a portion of an untranslated region.

A genomic region may comprise a gene. A genomic region may comprise onlya portion of a gene. A genomic region may comprise an exon of a gene. Agenomic region may comprise an intron of a gene. A genomic region maycomprise an untranslated region (UTR) of a gene. In some instances, agenomic region does not comprise an entire gene. A genomic region maycomprise less than 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%,40%, 35%, 30%, 25%, 20%, 15%, 10%, or 5% of a gene. A genomic region maycomprise less than 60% of a gene.

A genomic region may comprise a nonprotein-coding region. Anonprotein-coding region may also be referred to as a noncoding region.A nonprotein-coding region may refer to a region of the genome that doesnot encode for a protein. A nonprotein-coding region may be transcribedinto a noncoding RNA (ncRNA). The noncoding RNA may have a knownfunction. For example, the noncoding RNA may be a transfer RNA (tRNA),ribosomal RNA (rRNA), and/or regulatory RNA. The noncoding RNA may havean unknown function. Examples of ncRNA include, but are not limited to,tRNA, rRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA),microRNA, small interfering RNA (siRNAs), Piwi-interacting RNA (piRNA),and long ncRNA (e.g., Xist, HOTAIR). A genomic region may comprise apseudogene, transposon and/or retrotransposon.

A genomic region may comprise a recurrently mutated region. Arecurrently mutated region may refer to a region of the genome, usuallythe human genome, in which there is an increased probability of geneticmutation in a cancer of interest, relative to the genome as a whole. Arecurrently mutation region may refer to a region of the genome thatcontains one or more mutations that is recurrent in the population. Forexample, a recurrently mutation region may refer to a region of thegenome that contains a mutation that is present in two or more subjectsin a population. A recurrently mutated region may be characterized by a“Recurrence Index” (RI). The RI generally refers to the number ofindividual subjects (e.g., cancer patients) with a mutation that occurswithin a given kilobase of genomic sequence (e.g., number of patientswith mutations/genomic region length in kb). A genomic region may alsobe characterized by the number of patients with a mutation per exon.Thresholds for each metric (e.g. RI and patients per exon or genomicregion) may be selected to statistically enrich for known/suspecteddrivers of the cancer of interest. A known/suspected driver of thecancer of interest may be a gene. In non-small cell lung carcinoma(NSCLC), these metrics may enrich for known/suspected drivers (see geneslisted in Table 2). Thresholds can also be selected by arbitrarilychoosing the top percentile for each metric.

A selector set may comprise a genomic region comprising a mutation thatis not recurrent in the population. For example, a genomic region maycomprise one or more mutations that are present in a given subject. Insome instances, a genomic region that comprises one or more mutations ina subject may be used to produce a personalized selector set for thesubject.

The term “mutation” may refer to a genetic alteration in the genome ofan organism. For the purposes of the invention, mutations of interestare typically changes relative to the germline sequence, e.g. cancercell specific changes. Mutations may include single nucleotide variants(SNV), copy number variants (CNV), insertions, deletions andrearrangements (e.g., fusions). The selector set may comprise one ormore genomic regions comprising one or more mutations selected from agroup consisting of SNV, CNV, insertions, deletions, and rearrangements.The selector set may comprise a plurality of genomic regions comprisingtwo or more mutations selected from a group consisting of SNV, CNV,insertions, deletions, and rearrangements. The selector set may comprisea plurality of genomic regions comprising three or more mutationsselected from a group consisting of SNV, CNV, insertions, deletions, andrearrangements. The selector set may comprise a plurality of genomicregions comprising four or more mutations selected from a groupconsisting of SNV, CNV, insertions, deletions, and rearrangements. Theselector set may comprise a plurality of genomic regions comprising fiveor more mutations selected from a group consisting of SNV, CNV,insertions, deletions, and rearrangements. The selector set may comprisea plurality of genomic regions comprising at least one SNV, insertion,and deletion. The selector set may comprise a plurality of genomicregions comprising at least one SNV and rearrangement. The selector setmay comprise a plurality of genomic regions comprising at least oneinsertion, deletion, and rearrangement. The selector set may comprise aplurality of genomic regions comprising at least one deletion andrearrangement. The selector set may comprise a plurality of genomicregions comprising at least one insertion and rearrangement. Theselector set may comprise a plurality of genomic regions comprising atleast one SNV, insertion, deletion, and rearrangement. The selector setmay comprise a plurality of genomic regions comprising at least onerearrangement and at least one mutation selected from a group consistingof SNV, insertion, and deletion. The selector set may comprise aplurality of genomic regions comprising at least one rearrangement andat least one mutation selected from a group consisting of SNV, CNV,insertion, and deletion.

A selector set may comprise a mutation in a genomic region known to beassociated with a cancer. The mutation in a genomic region known to beassociated with a cancer may be referred to as a “known somaticmutation.” A known somatic mutation may be a mutation located in one ormore genes known to be associated with a cancer. A known somaticmutation may be a mutation located in one or more oncogenes. Forexample, known somatic mutations may include one or more mutationslocated in p53, EGFR, KRAS and/or BRCA1.

A selector set may comprise a mutation in a genomic region predicted tobe associated with a cancer. A selector set may comprise a mutation in agenomic region that has not been reported to be associated with acancer.

A genomic region may comprise a sequence of the human genome ofsufficient size to capture one or more recurrent mutations. The methodsof the invention may be directed at cfDNA, which is generally less thanabout 200 bp in length, and thus a genomic region may be generally lessthan about 10 kb. The length of genomics region in a selector set may beon average around about 100 bp, about 125 bp, about 150 bp, 175 bp,about 200 bp, about 225 bp, about 250 bp, about 275 bp, or around about300 bp. Generally the genomic region for a SNV can be quite short, fromabout 45 to about 500 bp in length, while the genomic region for afusion or other genomic rearrangement may be longer, from around about 1Kbp to about 10 Kbp in length. A genomic region in a selector set may beless than about 10 Kbp, 9 Kbp, 8 Kbp, 7 Kbp, 6 Kbp, 5 Kbp, 4 Kbp, 3 Kbp,2 Kbp, or 1 Kbp in length. A genomic region in a selector set may beless than about 1000 bp, 900 bp, 800 bp, 700 bp, 600 bp, 500 bp, 400 bp,300 bp, 200 bp, or 100 bp. A genomic region may be said to “identify” amutation when the mutation is within the sequence of that genomicregion.

In some embodiments, the total sequence covered by the selector set isless than about 1.5 megabase pairs (Mbp), 1.4 Mbp, 1.3 Mbp, 1.2 Mbp, 1.1Mbp, 1 Mbp. The total sequence covered by the selector set may be lessthan about 1000 kb, less than about 900 kb, less than about 800 kb, lessthan about 700 kb, less than about 600 kb, less than about 500 kb, lessthan about 400 kb, less than about 350 kb, less than about 300 kb, lessthan about 250 kb, less than about 200 kb, or less than about 150 kb.The total sequence covered by the selector set may be between about 100kb to 500 kb. The total sequence covered by the selector set may bebetween about 100 kb to 350 kb. The total sequence covered by theselector set may be between about 100 kb to 150 kb.

The selector set may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20 or more mutations in a plurality of genomicregions. The selector set may comprise 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 or more mutations in a plurality ofgenomic regions. The selector set may comprise 125, 150, 175, 200, 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000 or more mutations in a plurality of genomic regions.

At least a portion of the mutations may be within the same genomicregion. At least about 2, 3, 4, 5, 6, 7, 8, 9, 10 or more mutations maybe within the same genomic region. At least about 2 mutations may bewithin the same genomic region. At least about 3 mutations may be withinthe same genomic region.

At least a portion of the mutations may be within different genomicregions. At least about 2, 3, 4, 5, 6, 7, 8, 9, 10 or more mutations maybe within two or more different genomic regions. At least about 2mutations may be within two or more different genomic regions. At leastabout 3 mutations may be within two or more different genomic regions.

Two or more mutations may be in two or more different genomic regions ofthe same noncoding region. Two or more mutations may be in two or moredifferent genomic regions of the same protein-coding region. Two or moremutations may be in two or more different genomic regions of the samegene. For example, a first mutation may be located in a first genomicregion comprising a first exon of a first gene and a second mutation maybe located in a second genomic region comprising a second exon of thefirst gene. In another example, a first mutation may be located in afirst genomic region comprising a first portion of a first longnoncoding RNA and a second mutation may be located in a second genomicregion comprising a second portion of the first long noncoding RNA.

Alternatively, or additionally, two or more mutations may be in two ormore different genomic regions of two or more different noncodingregions, protein-coding regions, and/or genes. For example, a firstmutation may be located in a first genomic region comprising a firstexon of a first gene and a second mutation may be located in a secondgenomic region comprising a second exon of a second gene. In anotherexample, a first mutation may be located in a first genomic regioncomprising a first exon of a first gene and a second mutation may belocated in a second genomic region comprising a portion of a microRNA.

The selector set may identify a median of at least 2, usually at least3, and preferably at least 4 different mutations per individual subject.The selector set may identify a median of at least 5, 6, 7, 8, 9, 10,11, 12, 13 or more different mutations per individual subject. Thedifferent mutations may be in one or more genomic regions. The differentmutations may be in 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ormore genomic regions. The different mutations may be in 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or more recurrently mutated regions.

The median number of mutations identified by the selector set may bedetermined in a population of up to 10, up to 25, up to 25, up to 50, upto 87, up to 100 or more subjects. The median number of mutationsidentified by the selector set may be determined in a population of upto 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400 or moresubjects. In such a population, a selector set of interest may identifyone or more mutations in at least 60%, at least 65%, at least 70%, atleast 75%, at least 80%, at least 82%, at least 85%, at least 87%, atleast 90%, at least 92%, at least 95% or more of the subjects.

The total mutations identified by the selector set may be present in atleast 60%, at least 65%, at least 70%, at least 75%, at least 80%, atleast 82%, at least 85%, at least 87%, at least 90%, at least 92%, atleast 95% or more of subjects in a population. For example, the selectorset may identify a first mutation present in 20% of the subjects andsecond mutation in 80% of the subjects, thus the total mutationsidentified by the selector set may be present in 80% to 100% of thesubjects in the population.

In addition to a bioinformatics construct, a selector set can be used togenerate an oligonucleotide or set of oligonucleotides for specificcapture, sequencing and/or amplification of cfDNA corresponding to agenomic region. The set of oligonucleotides may include at least oneoligonucleotide for each genomic region that is to be targeted.Oligonucleotides may have the general characteristic of sufficientlength to uniquely identify the genomic region, e.g. usually at leastabout 15 nucleotides, at least about 16, 17, 18, 19, 20 nucleotides inlength. An oligonucleotide may further comprise an adapter for thesequencing system; a tag for sorting; a specific binding tag, e.g.biotin, FITC, etc. Oligonucleotides for amplification may comprise apair of sequences flanking the region of interest, and of oppositeorientation. The oligonucleotide may comprise a primer sequence. Theoligonucleotide may comprise a sequence that is complementary to atleast a portion of the genomic region.

The methods set forth herein may generate a bioinformatics constructcomprising the selector set sequence information. In order to use theselector set for patient diagnostic and prognostic methods, a set ofselector probes may be generated from the selector set library. The setof selector probes may comprise a sequence from at least about 20genomic regions, at least about 30 genomic regions, at least about 40genomic regions, at least about 50 genomic regions, at least about 60genomic regions, at least about 70 genomic regions, at least about 80genomic regions, at least about 90 genomic regions, at least about 100genomic regions, at least about 200 genomic regions, at least about 300genomic regions, at least about 400 genomic regions, or at least about500 genomic regions. The genomic regions may be selected from thegenomic regions set forth in any one of Tables 2 and 6-18. The selectionmay be based on bioinformatics criteria, including the additional valueprovided by the region, the RI, etc. In some embodiments a pre-setcoverage of patients is used as a cut-off, for example where at least90% have one or more of the SNV, where at least 95% have one or more ofthe SNV, where at least 98% have one or more of the SNV.

The selector set may comprise one or more genomic regions identified byTable 2. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 2. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500,or 525 regions from those identified in Table 2. The genomic regions ofthe selector set may comprise at least 2 regions from those identifiedin Table 2. The genomic regions of the selector set may comprise atleast 20 regions from those identified in Table 2. The genomic regionsof the selector set may comprise at least 60 regions from thoseidentified in Table 2. The genomic regions of the selector set maycomprise at least 100 regions from those identified in Table 2. Thegenomic regions of the selector set may comprise at least 300 regionsfrom those identified in Table 2. The genomic regions of the selectorset may comprise at least 400 regions from those identified in Table 2.The genomic regions of the selector set may comprise at least 500regions from those identified in Table 2.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 2. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 2. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 2. At least about 20% of the genomicregions of the selector set may be regions identified in Table 2. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 2. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 2.

The selector set may comprise one or more genomic regions identified byTable 6. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 6. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500,550, 600, 650, 700, 750, 800, or 830 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 6. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 6. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 6. The genomic regions of theselector set may comprise at least 600 regions from those identified inTable 6. The genomic regions of the selector set may comprise at least800 regions from those identified in Table 6.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 6. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 6. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 6. At least about 20% of the genomicregions of the selector set may be regions identified in Table 6. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 6. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 6.

The selector set may comprise one or more genomic regions identified byTable 7. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 7. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300,325, 350, 375, 400, 425, or 450 regions from those identified in Table7. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 7. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 7. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 7. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 7. The genomic regions of the selector set may comprise at least200 regions from those identified in Table 7. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 7. The genomic regions of the selector set may comprise at least400 regions from those identified in Table 7.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 7. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 7. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 7. At least about 20% of the genomicregions of the selector set may be regions identified in Table 7. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 7. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 7.

The selector set may comprise one or more genomic regions identified byTable 8. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 8. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table8. The genomic regions of the selector set may comprise at least 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000, or 1050 regions from those identified in Table 8. The genomicregions of the selector set may comprise at least 2 regions from thoseidentified in Table 8. The genomic regions of the selector set maycomprise at least 20 regions from those identified in Table 8. Thegenomic regions of the selector set may comprise at least 60 regionsfrom those identified in Table 8. The genomic regions of the selectorset may comprise at least 100 regions from those identified in Table 8.The genomic regions of the selector set may comprise at least 300regions from those identified in Table 8. The genomic regions of theselector set may comprise at least 600 regions from those identified inTable 8. The genomic regions of the selector set may comprise at least800 regions from those identified in Table 8. The genomic regions of theselector set may comprise at least 1000 regions from those identified inTable 8.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 8. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 8. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 8. At least about 20% of the genomicregions of the selector set may be regions identified in Table 8. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 8. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 8.

The selector set may comprise one or more genomic regions identified byTable 9. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 9. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table9. The genomic regions of the selector set may comprise at least 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000, 1100, 1200, 1300, 1400, or 1500 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least 2regions from those identified in Table 9. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 9. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 9. The genomic regions of theselector set may comprise at least 500 regions from those identified inTable 9. The genomic regions of the selector set may comprise at least1000 regions from those identified in Table 9. The genomic regions ofthe selector set may comprise at least 1300 regions from thoseidentified in Table 9.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 9. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 9. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 9. At least about 20% of the genomicregions of the selector set may be regions identified in Table 9. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 9. At least about 40% of the genomic regionsof the selector set may be regions identified in Table 9.

The selector set may comprise one or more genomic regions identified byTable 10. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 10. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table10. The genomic regions of the selector set may comprise at least 125,130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195,200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or 330regions from those identified in Table 10. The genomic regions of theselector set may comprise at least 2 regions from those identified inTable 10. The genomic regions of the selector set may comprise at least20 regions from those identified in Table 10. The genomic regions of theselector set may comprise at least 60 regions from those identified inTable 10.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 10. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 10. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 10. At least about 20% of the genomicregions of the selector set may be regions identified in Table 10. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 10. At least about 40% of the genomicregions of the selector set may be regions identified in Table 10.

The selector set may comprise one or more genomic regions identified byTable 11. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 11. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table11. The genomic regions of the selector set may comprise at least 125,130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195,200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330,340, 350, 375, 400, 420, 440, or 460 regions from those identified inTable 11. The genomic regions of the selector set may comprise at least2 regions from those identified in Table 11. The genomic regions of theselector set may comprise at least 20 regions from those identified inTable 11. The genomic regions of the selector set may comprise at least60 regions from those identified in Table 11. The genomic regions of theselector set may comprise at least 100 regions from those identified inTable 11. The genomic regions of the selector set may comprise at least200 regions from those identified in Table 11. The genomic regions ofthe selector set may comprise at least 300 regions from those identifiedin Table 11. The genomic regions of the selector set may comprise atleast 400 regions from those identified in Table 11.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 11. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 11. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 11. At least about 20% of the genomicregions of the selector set may be regions identified in Table 11. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 11. At least about 40% of the genomicregions of the selector set may be regions identified in Table 11.

The selector set may comprise one or more genomic regions identified byTable 12. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 12. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table12. The genomic regions of the selector set may comprise at least 125,130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195,200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330,340, 350, 375, 400, 420, 440, 460, 480 or 500 regions from thoseidentified in Table 12. The genomic regions of the selector set maycomprise at least 2 regions from those identified in Table 12. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 12. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 12.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 12. The genomic regions of theselector set may comprise at least 200 regions from those identified inTable 12. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 12. The genomic regions ofthe selector set may comprise at least 400 regions from those identifiedin Table 12. The genomic regions of the selector set may comprise atleast 500 regions from those identified in Table 12.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 12. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 12. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 12. At least about 20% of the genomicregions of the selector set may be regions identified in Table 12. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 12. At least about 40% of the genomicregions of the selector set may be regions identified in Table 12.

The selector set may comprise one or more genomic regions identified byTable 13. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 13. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table13. The genomic regions of the selector set may comprise at least 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450 regionsfrom those identified in Table 13. The genomic regions of the selectorset may comprise at least 2 regions from those identified in Table 13.The genomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 13. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 13.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 13. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 13. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 13. The genomic regions ofthe selector set may comprise at least 1000 regions from thoseidentified in Table 13. The genomic regions of the selector set maycomprise at least 1300 regions from those identified in Table 13.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 13. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 13. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 13. At least about 20% of the genomicregions of the selector set may be regions identified in Table 13. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 13. At least about 40% of the genomicregions of the selector set may be regions identified in Table 13.

The selector set may comprise one or more genomic regions identified byTable 14. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 14. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table14. The genomic regions of the selector set may comprise at least 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000, 1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regions fromthose identified in Table 14. The genomic regions of the selector setmay comprise at least 2 regions from those identified in Table 14. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 14. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 14.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 14. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 14. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 14. The genomic regions ofthe selector set may comprise at least 1000 regions from thoseidentified in Table 14. The genomic regions of the selector set maycomprise at least 1100 regions from those identified in Table 14. Thegenomic regions of the selector set may comprise at least 1200 regionsfrom those identified in Table 14.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 14. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 14. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 14. At least about 20% of the genomicregions of the selector set may be regions identified in Table 14. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 14. At least about 40% of the genomicregions of the selector set may be regions identified in Table 14.

The selector set may comprise one or more genomic regions identified byTable 15. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 15. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, or 170regions from those identified in Table 15. The genomic regions of theselector set may comprise at least 2 regions from those identified inTable 15. The genomic regions of the selector set may comprise at least20 regions from those identified in Table 15. The genomic regions of theselector set may comprise at least 60 regions from those identified inTable 15. The genomic regions of the selector set may comprise at least100 regions from those identified in Table 15. The genomic regions ofthe selector set may comprise at least 120 regions from those identifiedin Table 15. The genomic regions of the selector set may comprise atleast 150 regions from those identified in Table 15.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 15. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 15. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 15. At least about 20% of the genomicregions of the selector set may be regions identified in Table 15. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 15. At least about 40% of the genomicregions of the selector set may be regions identified in Table 15.

The selector set may comprise one or more genomic regions identified byTable 16. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 16. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table16. The genomic regions of the selector set may comprise at least 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or2050 regions from those identified in Table 16. The genomic regions ofthe selector set may comprise at least 2 regions from those identifiedin Table 16. The genomic regions of the selector set may comprise atleast 20 regions from those identified in Table 16. The genomic regionsof the selector set may comprise at least 60 regions from thoseidentified in Table 16. The genomic regions of the selector set maycomprise at least 100 regions from those identified in Table 16. Thegenomic regions of the selector set may comprise at least 300 regionsfrom those identified in Table 16. The genomic regions of the selectorset may comprise at least 500 regions from those identified in Table 16.The genomic regions of the selector set may comprise at least 1000regions from those identified in Table 16. The genomic regions of theselector set may comprise at least 1200 regions from those identified inTable 16. The genomic regions of the selector set may comprise at least1500 regions from those identified in Table 16. The genomic regions ofthe selector set may comprise at least 1700 regions from thoseidentified in Table 16. The genomic regions of the selector set maycomprise at least 2000 regions from those identified in Table 16.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 16. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 16. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 16. At least about 20% of the genomicregions of the selector set may be regions identified in Table 16. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 16. At least about 40% of the genomicregions of the selector set may be regions identified in Table 16.

The selector set may comprise one or more genomic regions identified byTable 17. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 17. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table17. The genomic regions of the selector set may comprise at least 250,300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950,1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regions fromthose identified in Table 17. The genomic regions of the selector setmay comprise at least 2 regions from those identified in Table 17. Thegenomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 17. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 17.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 17. The genomic regions of theselector set may comprise at least 300 regions from those identified inTable 17. The genomic regions of the selector set may comprise at least500 regions from those identified in Table 17. The genomic regions ofthe selector set may comprise at least 1000 regions from thoseidentified in Table 17. The genomic regions of the selector set maycomprise at least 1050 regions from those identified in Table 17.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 17. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 17. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 17. At least about 20% of the genomicregions of the selector set may be regions identified in Table 17. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 17. At least about 40% of the genomicregions of the selector set may be regions identified in Table 17.

The selector set may comprise one or more genomic regions identified byTable 18. The genomic regions of the selector set may comprise at least2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore regions from those identified in Table 18. The genomic regions ofthe selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60,65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table18. The genomic regions of the selector set may comprise at least 125,130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195,200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330,340, 350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555 regionsfrom those identified in Table 18. The genomic regions of the selectorset may comprise at least 2 regions from those identified in Table 18.The genomic regions of the selector set may comprise at least 20 regionsfrom those identified in Table 18. The genomic regions of the selectorset may comprise at least 60 regions from those identified in Table 18.The genomic regions of the selector set may comprise at least 100regions from those identified in Table 18. The genomic regions of theselector set may comprise at least 200 regions from those identified inTable 18. The genomic regions of the selector set may comprise at least300 regions from those identified in Table 18. The genomic regions ofthe selector set may comprise at least 400 regions from those identifiedin Table 18. The genomic regions of the selector set may comprise atleast 500 regions from those identified in Table 18.

At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%,60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of theselector set may be regions identified in Table 18. At least about 5% ofthe genomic regions of the selector set may be regions identified inTable 18. At least about 10% of the genomic regions of the selector setmay be regions identified in Table 18. At least about 20% of the genomicregions of the selector set may be regions identified in Table 18. Atleast about 30% of the genomic regions of the selector set may beregions identified in Table 18. At least about 40% of the genomicregions of the selector set may be regions identified in Table 18.

Selector set probes may be at least about 15, 20, 25, 30, 35, 40, 45,50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100 nucleotides in length.Selector set probes may be at least about 20 nucleotides in length.Selector set probes may be at least about 30 nucleotides in length.Selector set probes may be at least about 40 nucleotides in length.Selector set probes may be at least about 50 nucleotides in length.

Selector probes may be of about 15 to about 250 nucleotides in length.Selector set probes may be about 15 to about 200 nucleotides in length.Selector set probes may be about 15 to about 170 nucleotides in length.Selector set probes may be about 15 to about 150 nucleotides in length.Selector set probes may be about 25 to about 200 nucleotides in length.Selector set probes may be about 25 to about 150 nucleotides in length.Selector set probes may be about 50 to about 150 nucleotides in length.Selector set probes may be about 50 to about 125 nucleotides in length.

1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more selector set probes may correspondto one genomic region. Two or more selector set probes may correspond toone genomic region. Three or more selector set probes may correspond toone genomic region. A set of selector set probes therefore may have thecomplexity of the selector set from which it is obtained. Selectorprobes may be synthesized using conventional methods, or generated byany other suitable molecular biology approach. Selector probes may behybridized to cfDNA for hybrid capture, as described herein. Selectorprobes may comprise a binding moiety that allows capture of the hybrid.Various binding moieties (e.g., tags) useful for this purpose are knownin the art, including without limitation biotin, HIS tags, MYC tags,FITC, and the like.

Exemplary selector sets are provided in Tables 2, and 6-18. The selectorset comprising one or more genomic regions identified in Table 2 may beuseful for non-small cell lung carcinoma (NSCLC). The selector setcomprising one or more genomic regions identified in Table 6 may beuseful for breast cancer. The selector set comprising one or moregenomic regions identified in Table 7 may be useful for colorectalcancer. The selector set comprising one or more genomic regionsidentified in Table 8 may be useful for diffuse large B-cell lymphoma(DLBCL). The selector set comprising one or more genomic regionsidentified in Table 9 may be useful for Ehrlich ascites carcinoma (EAC).The selector set comprising one or more genomic regions identified inTable 10 may be useful for follicular lymphoma (FL). The selector setcomprising one or more genomic regions identified in Table 11 may beuseful for head and Neck squamous cell carcinoma (HNSC). The selectorset comprising one or more genomic regions identified in Table 12 may beuseful for NSCLC. The selector set comprising one or more genomicregions identified in Table 13 may be useful for NSCLC. The selector setcomprising one or more genomic regions identified in Table 14 may beuseful for ovarian cancer. The selector set comprising one or moregenomic regions identified in Table 15 may be useful for ovarian cancer.The selector set comprising one or more genomic regions identified inTable 16 may be useful for pancreatic cancer. The selector setcomprising one or more genomic regions identified in Table 17 may beuseful for prostate adenocarcinoma. The selector set comprising one ormore genomic regions identified in Table 18 may be useful for skincutaneous melanoma. The selector set of any one of Tables 2 and 6-18 maybe useful for carcinomas and sub-generically for adenocarcinomas orsquamous cell carcinomas.

Methods for Producing a Selector Set

Disclosed herein are methods of producing a selector set. One objectivein designing a selector set may comprise maximizing the fraction ofpatients covered and the number of mutations per patient covered whileminimizing selector size. Evaluating all possible combinations ofgenomic regions to build such a selector set may be an exponentiallylarge problem (e.g., 2^(n) possible exon combinations given n exons),rendering the use of an approximation algorithm critical. Thus, aheuristic strategy may be used to produce a selector set.

The selector sets disclosed herein may be rationally designed for agiven ctDNA detection limit, sequencing cost, and/or DNA input mass.Such a selector set may be designed using a selector design calculator.A selector design calculator may be based on the following analyticalmodel: the probability P of recovering at least 1 read of a singlemutant allele in plasma for a given sequencing read depth and detectionlimit of ctDNA in plasma may be modeled by a binomial distribution.Given P, the probability of detecting all identified tumor mutations inplasma may be modeled by a geometric distribution. With this designcalculator, one can first estimate how many tumor reporters will beneeded to achieve a desired sensitivity, and can then target a selectorsize that balances this number with considerations of cost and DNA massinput. FIG. 5 a shows a graphical representation of the probability P ofdetecting ctDNA in plasma for different detection limits of ctDNA inplasma for CAPP-Seq (dark, thick line), whole exome sequence (i and ii),and whole genome sequence (iii).

The method of producing a selector set may comprise (a) calculating arecurrence index of a genomic region of a plurality of genomic regionsby dividing a number of subjects that have one or more mutations in thegenomic region by a length of the genomic region; and (b) producing aselector set comprising one or more genomic regions of the plurality ofgenomic regions by selecting genomic regions based on the recurrenceindex. For example, 10 subjects may contain one or more mutations in agenomic region comprising 100 bases. The recurrence index could becalculated by dividing the number of subjects containing mutations inthe one or more genomic regions by the length of the genomic region. Inthis example, the recurrence index for this genomic region would be 10subjects divided by 100 bases, which equals 0.1 subjects per base.

The method may further comprise ranking genomic regions of the pluralityof genomic regions by the recurrence index. Producing the selector setbased on the recurrence index may comprise selecting genomic regionsthat have a recurrence index in the top 70^(th), 75^(th), 80^(th),85^(th), 90 ^(th), or 95^(th) or greater percentile. Producing theselector set based on the recurrence index may comprise selectinggenomic regions that has a recurrence index in the top 90^(th)percentile. For example, a first genomic region may have a recurrenceindex in the top 80^(th) percentile and a second genomic region may havea recurrence index in the bottom 20^(th) percentile. The selector setbased on genomic regions with a recurrence index in the top 75^(th)percentile may comprise the first genomic region, but not the secondgenomic region.

The method may further comprise ranking the genomic regions by thenumber of subjects having one or more mutations in the genomic region.Producing the selector set may further comprise selecting genomicregions in the top 70^(th), 75^(th), 80^(th), 85^(th), 90^(th), or95^(th) or greater percentile of number of subjects having one or moremutations in the genomic region. Producing the selector set may furthercomprise selecting genomic regions in the top 90^(th) or greaterpercentile of number of subjects having one or more mutations in thegenomic region.

The length of the genomic region may be in kilobases. The length of thegenomic region may be in bases. For genomic regions containing knownsomatic mutations associated with a cancer, the length of the genomicregion may consist essentially on the subsequence of the known mutation.For genomic regions containing known somatic mutations associated with acancer, the length of the genomic region may consist essentially on thesubsequence of the known mutation and one or more bases flanking thesubsequence of the known mutation. For genomic regions containing knownsomatic mutations associated with a cancer, the length of the genomicregion may consist essentially on the subsequence of the known mutationand 1 to 5 bases flanking the subsequence of the known mutation. Forgenomic regions containing known somatic mutations associated with acancer, the length of the genomic region may consist essentially on thesubsequence of the known mutation and 5 or fewer bases flanking thesubsequence of the known mutation. The recurrence index for a genomicregion comprising a known somatic mutation may be recalculated based onthe length of the subsequence of the known mutation or the length of thesubsequence of the known mutation with additional bases flanking thesubsequence of the known mutation. For example, a genomic region maycomprise 200 bases and the known somatic mutation within the genomicregion may comprise 100 bases. The recurrence index may be calculated bydividing the number of subjects containing one or more mutations in thegenomic region divided by the length of the somatic mutation with thegenomic region (e.g., 100 bases).

Further disclosed herein is a method of producing a selector setcomprising (a) identifying, with the aid of a computer processor, aplurality of genomic regions comprising one or more mutations byanalyzing data pertaining to the plurality of genomic regions from apopulation of subjects suffering from a cancer; and (b) applying analgorithm to the data to produce a selector set comprising two or moregenomic regions of the plurality of genomic regions, wherein thealgorithm is used to maximize a median number of mutations in thegenomic regions of the selector set in the population of subjects.

Identifying the plurality of genomic regions may comprise calculating arecurrence index of one or more genomic regions of the plurality ofgenomic regions. The algorithm may be applied to the data pertaining togenomic regions with a recurrence index in the top 40^(th), 45^(th),50^(th), 55^(th), 57^(th), 60^(th), 63^(rd), or 65^(th) or higherpercentile. The algorithm may be applied to data pertaining to genomicregions having a recurrence index of at least about 15, 20, 25, 30, 35,40, 45, or 50 or more.

Identifying the plurality of genomic regions may comprise determining anumber of subjects having one or more mutations in a genomic region. Thealgorithm may be applied to the data pertaining to genomic regions inthe top 40^(th), 45^(th), 50^(th), 55^(th), 57^(th), 60^(th), 63^(rd),or 65^(th) or greater percentile of number of subjects having one ormore mutations in the genomic region

The algorithm may maximize the median number of mutations by identifyinggenomic regions that result in the largest reduction in subjects withone mutation in the genomic region. Producing the selector set maycomprise selecting genomic regions that result in the largest reductionin subjects with one mutation in the genomic region.

The algorithm may be applied to the data pertaining to genomic regionsmeeting a minimum threshold. The minimum threshold may pertain to therecurrence index. For example, the algorithm may be applied to genomicregions having a recurrence index in the top 60^(th) percentile. Inanother example, the algorithm may be applied to genomic regions thathave a recurrence index of greater than or equal to 30. Alternatively,or additionally, the minimum threshold may pertain to genomic regions inthe top 60^(th) percentile of the number of subjects having one or moremutations in the genomic region.

The algorithm may be applied 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or moretimes. The algorithm may be applied one or more times. The algorithm maybe applied two or more times. The algorithm may be applied to a firstset of genomic regions meeting a first minimum threshold. For example,the algorithm may be applied to a first set of genomic regions in thetop 60^(th) percentile of the recurrence index and the top 60^(th)percentile of the number of subjects having one or more mutations in thegenomic region. The algorithm may be applied a second set of genomicregions meeting a second minimum threshold. For example, the algorithmmay be applied to a second set of genomic regions having a recurrenceindex of greater than or equal to 20.

The median number of mutations in the genomic regions in the populationof subjects may be at least about 2, 3, 4, 5, 6, 7, 8, 9, 10 or moremutations. The median number of mutations in the genomic regions in thepopulation of subjects may be at least about 2, 3, or 4 or moremutations.

The algorithm may further be used to maximize a number of subjectscontaining one or more mutations within the genomic regions in theselector set. The algorithm may further be used to maximize a percentageof subjects from the population containing the one or more mutationswithin the genomic regions in the selector set. The percentage ofsubjects from the population containing the one or more mutations withinthe genomic regions may be at least about 60%, 65%, 70%, 75%, 80%, 85%,87%, 90%, 92%, 95%, or 97% or more.

Alternatively, the method of producing a selector set may comprise (a)obtaining data pertaining to a plurality of genomic regions from apopulation of subjects suffering from a cancer; and (b) applying analgorithm to the data to produce a selector set comprising two or moregenomic regions of the plurality of genomic regions, wherein thealgorithm is used to maximize a number of subjects containing one ormore mutations within the genomic regions in the selector.

The algorithm may maximize the number of subjects containing the one ormore mutations by calculating a recurrence index of the genomic regions.Producing the selector set may comprise selecting one or more genomicregions based on the recurrence index.

The algorithm may maximize the number of subjects containing the one ormore mutations by identifying genomic regions comprising one or moremutations found in 2, 3, 4, 5, 6, 7, 8, 9, 10 or more subjects. Thealgorithm may maximize the number of subjects containing the one or moremutations by identifying genomic regions comprising one or moremutations found in 5 or more subjects. Producing the selector set maycomprise selecting one or more genomic regions based on a frequency ofthe mutation within the genomic region in the population of subjects.

Producing the selector set may comprise iterative addition of thegenomic regions to the selector set. Producing the selector set maycomprise selecting one or more genomic regions that identify mutationsin at least one new subject from the population of subjects. Forexample, a selector set may comprise genomic regions A, B, and C, whichcontain mutations observed in subjects 1, 2, 3, 4, 5, 6, 7 and 8.Genomic region D may contain a mutation observed in subjects 1-4 and 10.Genomic region E may contain a mutation observed in subjects 1-5.Genomic region D identified at least one additional subject (e.g.,subject 10) and may be added to the selector set, whereas genomic regionE did not identify an additional subject and is not added to theselector set.

Producing the selector set may comprise selecting one or more genomicregions based on minimizing overlap of subjects already identified bythe selector. For example, a selector set may comprise genomic regionsA, B, C, and D, which contain mutations observed in subjects 1, 2, 3, 4,5, 6, 7, 8, 9, and 10. Genomic region E may contain a mutation observedin subjects 1-5, 11, and 13. Genomic region F may contain a mutationobserved in subjects 12 and 15. Genomic region E had 5 subjects incommon with the selector set, whereas genomic region F had no subjectsin common with the selector set. Thus, genomic region F may be added tothe selector set.

The algorithm may be used to maximize a percentage of subjects from thepopulation containing the one or more mutations within the genomicregions in the selector. The percentage of subjects from the populationcontaining the one or more mutations within the genomic regions may beat least about 60%, 65%, 70%, 75%, 80%, 85%, 87%, 90%, 92%, 95%, or 97%or more.

The algorithm may further be used to maximize a median number ofmutations in the genomic regions in a subject of the population ofsubjects. The median number of mutations in the genomic regions in thesubject may be at least about 2, 3, 4, 5, 6, 7, 8, 9, 10 or moremutations. The median number of mutations in the genomic regions in thesubject may be at least about 2, 3, or 4 or more mutations.

Producing the selector set may further comprise adding genomic regionscomprising one or more mutations known to be associated with a cancer.Producing the selector set may further comprise adding genomic regionscomprising one or more mutations predicted to be associated with acancer. Producing the selector set may further comprise adding genomicregions comprising one or more rearrangements. Producing the selectorset may further comprise adding genomic regions comprising one or morefusions.

The method may further comprise identifying one or more genomic regionsthat contain one or more recurrent mutations in a cancer. Theidentification of these recurrent mutations may benefit greatly from theavailability of databases such as, for example, The Cancer Genome Atlas(TCGA) and its subsets. Such databases may serve as the starting pointfor identifying the recurrently mutated genomic regions of the selectorsets. The databases may also provide a sample of mutations occurringwithin a given percentage of subjects with a specific cancer.

The method of producing a selector set may comprise (a) identifying aplurality of genomic regions; (b) prioritizing the plurality of genomicregions; and (c) selecting one or more genomic regions for inclusion ina selector set. The following design strategy can be used to identifyand prioritize genomic regions for inclusion in a selector set. Threephases may incorporate known and suspected driver genes, as well asgenomic regions known to participate in clinically actionable fusions,while another three phases may employ an algorithmic approach tomaximize both the number of patients covered and SNVs per patient,utilizing the “Recurrence Index” (RI) as described herein. The strategymay utilize an initial patient database to evaluate the utility ofincluding genomic regions in the selector set. A typical database forthis purpose may include sequence information from at least 25, at least50, at least 100, at least 200, at least 300 or more individual tumors.The method for producing a selector set may comprise one or more of thefollowing phases:

-   -   Phase 1 (Known drivers). Genes known to be drivers in the cancer        of interest are selected based on the pattern of SNVs previously        identified in tumors.    -   Phase 2 (Maximize coverage). To maximize coverage, for each exon        with SNVs covering ≧5 cancer patients in the starting database,        select the exon with highest RI that identified at least 1 new        patient when compared to the prior phase. Among exons with        equally high RI, add the exon with minimum overlap among        patients already captured by the selector. Repeat until no        further exons met these criteria.    -   Phase 3 (RI≧30). For each remaining exon with an RI≧30 and with        SNVs covering ≧3 patients in the relevant database, identify the        exon that results in the largest reduction in patients with only        1 SNV. To break ties among equally best exons, the exon with        highest RI was chosen. This was repeated until no additional        exons satisfied these criteria.    -   Phase 4 (RI≧20). Repeat the procedure in Phase 3, but using        RI≧20.    -   Phase 5 (Predicted drivers). Add in all exons from additional        genes previously predicted to harbor driver mutations in the        cancer of interest.    -   Phase 6 (Add fusions). Add in for known recurrent rearrangements        the introns most frequently implicated in the fusion event and        the flanking exons.

It should be understood, however, that the addition of known drivers,predicted drivers and fusions can be performed independently and in anyorder.

A method of producing a selector set may comprise (a) calculating arecurrence index for a plurality of genomic regions from a population ofsubjects suffering from a cancer by dividing a number of subjectscontaining one or more mutations in a genomic region of the plurality ofgenomic regions by a size of the genomic region; and (b) ranking theplurality of genomic regions based on their recurrence index.

A method of producing a selector set may comprise (a) calculating arecurrence index for a plurality of genomic regions from a population ofsubjects suffering from a cancer by dividing a number of subjectscontaining one or more mutations in a genomic region of the plurality ofgenomic regions by a size of the genomic region; and (b) producing aselector set comprising two or more genomic regions of the plurality ofgenomic regions by (i) using the recurrence index to maximize coverageof the selector set for the population of subjects; and/or (ii) usingthe recurrence index to maximize a median number of mutations persubject in the population of subjects.

Maximizing subject coverage may comprise use of a metric termed“Recurrence Index” (RI). The RI may refer to the number of subjects thatharbor mutations (e.g., SNVs/indels) in a given kilobase of genomicsequence. This metric can be further normalized by the number ofsubjects per study to allow comparison of different studies and distinctcancers. A similar approach was used to produce a selector set fornon-small cell lung cancer (NSCLC) (see FIG. 1 b). For one exemplaryNSCLC selector set, exons were the primary genomic unit and indels werenot considered. A portion of an exon may contain known somaticmutations. In this case, the algorithm only includes the subsequence ofthe portion of the exon containing known lesions flanked by auser-defined buffer (by default, =1 base). RI may be recalculated foreach exon following this adjustment. The algorithm may rank genomicregions by decreasing RI. The algorithm may consider a subset of thegenomic regions. For example, the algorithm may only consider genomicregions in the top P percentile of both RI and/or the number of subjectsper exon (P=90^(th) percentile by default, but is user modifiable).Selector design may proceed by iteratively traversing the list of rankedgenomic regions, selecting each genomic region that adds additionalsubject coverage with minimal additional space. This may continue untilall genomic regions satisfying percentile filters have been evaluatedand/or a user-defined maximum selector size has been reached.

Producing the selector set may comprise maximizing the median number ofmutations per subject. Maximizing the median number of mutations persubject may comprise use of one or more algorithms. Maximizing themedian number of mutations per subject may comprise use of one or morethresholds or filters to evaluate the genomic regions for inclusion inthe selector set. The thresholds or filters may be based on therecurrence index. For example, the filter may be a percentile filter ofthe recurrence index. The percentile filters may be relaxed to permitthe assessment of additional genomic regions for inclusion in theselector set. The percentile filter may be set at (⅔)×P, where P is atop percentile of RI. The threshold may be user-defined. The thresholdmay be greater than or equal to ⅔. Alternatively, the threshold is lessthan or equal to ⅔. P may also be user-defined. The algorithm mayproceed through the list of genomic regions ranked by decreasing RI,iteratively adding regions that maximally increase the median number ofmutations per subject. The process may terminate after assessing allgenomic regions that pass percentile filters, and/or if the desiredselector size endpoint is reached. This process may be repeated for athird round or more by continuing to relax the percentile threshold.Maximizing the median number of mutations per subject may comprise (i)ranking two or more genomic regions based on their recurrence index;(ii) producing a list of genomic regions comprising a subset of thegenomic regions, wherein the genomic regions in the list have arecurrence index in the top 60^(th) percentile; and (iii) producing apreliminary selector set by adding genomic regions to the preliminaryselector set and calculating a median number of mutations per subject inthe preliminary selector set.

Further disclosed herein is a method of producing a selector setcomprising (a) obtaining data pertaining to one or more genomic regions;(b) applying an algorithm to the data to determine for a genomic region:(i) a presence of one or more mutations in the genomic region; (ii) anumber of subjects with mutations in that genomic region; and (iii) arecurrence index (RI), wherein the RI is determined by dividing thenumber of subjects with mutations in the genomic region by the size ofgenomic region; and (c) producing a selector set comprising one or moregenomic regions based on the recurrence index of the one or more genomicregions.

The method may further comprise recalculating the recurrence index forone or more genomic regions comprising known mutations. The size of theknown mutation may be less than the size of the genomic region.Recalculating the recurrence index may comprise dividing the number ofsubjects with known mutations in the genomic region by the size of theknown mutation. For example, the size of a genomic region may be 200basepairs and the size of the known mutation within the genomic regionmay be 100 basepairs. The recurrence index for the genomic region may bedetermined by dividing the number of subjects with the known mutation inthe genomic region by the size of the known mutation (e.g., 100 basepairs) rather than dividing by the size of the entire genomic region(e.g., 200 base pairs).

The method may further comprise ranking the two or more genomic regionsbased on the recurrence index. The list of ranked genomic regions maycomprise a subset of the genomic regions ranked by the recurrence index.The list of ranked genomic regions may comprise a subset of the genomicregions that satisfy one or more criteria. The one or more criteria maybe based on the recurrence index. For example, the list of rankedgenomic regions may comprise a subset of genomic regions that have arecurrence index in the top 90^(th) percentile. Producing the selectorset may comprise selecting the one or more genomic regions based on therecurrence index. Producing the selector set may comprise selecting theone or more genomic regions based on the rank of the two or more genomicregions. The two or more genomic regions may be ranked with the aid ofan algorithm. The algorithm used to rank the two or more genomic regionsbased on the recurrence may be the same algorithm used to determine therecurrence index of the one or more genomic regions. The algorithm maybe a different from the algorithm used to determine the recurrenceindex.

The method may further comprise iteratively traversing a list of rankedgenomic regions and selecting genomic regions that provide additionalsubject coverage with minimal addition to the total size of the genomicregions of a proposed selector set. For example, a first genomic regionmay add two new subjects to the proposed selector set and the size ofthe proposed selector set may increase by 10 base pairs, whereas asecond genomic region may add two new subjects to the proposed selectorset and the size of the proposed selector set may increase by 100 basepairs. The first genomic region may be selected over the second genomicregion for inclusion in the proposed selector set. The entire list ofranked genomic regions may be traversed. Alternatively, a portion of thelist of ranked genomic regions may be traversed. For example, thetraversal and selection of genomic regions may be based on auser-defined maximum selector size. Once the maximum selector size hasbeen reached, the step of traversing the list of ranked genomic regionsand selecting genomic regions may be terminated. An algorithm may beused to traverse the list of ranked genomic regions and to selectgenomic regions for inclusion in the selector set. The algorithm may bethe same algorithm used to determine the recurrence index. The algorithmmay be a different from the algorithm used to determine the recurrenceindex.

The method may further comprise iteratively traversing a list of rankedgenomic regions and selecting genomic regions that maximize the mediannumber of mutations per subject in the population of subjects of theselector set. The median number of mutations per subject for a proposedselector set may be determined by (a) counting a number of mutations Nin each subject across all genomic regions for the proposed selectorset; and (b) applying an algorithm to identify the median number ofmutations by sorting the subjects by the number of mutations. Forexample, a proposed selector set may comprise 10 genomic regionscomprising 20 mutations in a population of 9 subjects. A first subjectmay have 4 mutations, a second subject may have 2 mutations, a thirdsubject may have 3 mutations, a fourth subject may have 6 mutations, afifth subject have may 8 mutations, a sixth subject may have 6mutations, a seventh subject may have eight mutations, an eighth subjectmay have 4 mutations, and a ninth subject may have two mutations. Themedian of {2, 2, 3, 4, 4, 6, 8, 8} is 4. A genomic region may beselected for inclusion in the selector set if the inclusion of thegenomic region increases the median number of mutations per subject inthe population of subjects in the selector set. For example, a firstgenomic region may contain one mutation present in two of the tensubjects and second genomic region may contain one mutation present inthree of the ten subjects. The second genomic region may be selected forinclusion into the selector set over the first genomic region becauseaddition of the second genomic region to the selector set would resultin a greater increase the median number of mutations per subject thanaddition of the first genomic region. The entire list of ranked genomicregions may be traversed. Alternatively, a portion of the list of rankedgenomic regions may be traversed. For example, the traversal andselection of genomic regions may be based on a user-defined maximumselector size. Once the maximum selector size has been reached, the stepof traversing the list of ranked genomic regions and selecting genomicregions may be terminated.

Methods of producing a selector set may comprise: (a) obtainingsequencing information of a tumor sample from a subject suffering from acancer; (b) comparing the sequencing information of the tumor sample tosequencing information from a non-tumor sample from the subject toidentify one or more mutations specific to the sequencing information ofthe tumor sample; and (c) producing a selector set comprising one ormore genomic regions comprising the one or more mutations specific tothe sequencing information of the tumor sample. The selector set maycomprise sequencing information pertaining to the one or more genomicregions. The selector set may comprise genomic coordinates pertaining tothe one or more genomic regions. The selector set may comprise aplurality of oligonucleotides that selectively hybridize the one or moregenomic regions. The plurality of oligonucleotides may be biotinylated.The one or more mutations comprise SNVs. The one or more mutationscomprise indels. The one or more mutations comprise rearrangements.Producing the selector set may comprise identifying tumor-derived SNVsbased on the methods disclosed herein. Producing the selector set maycomprise identifying tumor-derived rearrangements based on the methodsdisclosed herein.

Application of the approaches described herein for mutated genomicregions in non-small cell lung cancer may result in the selector setshown in Table 2. The selector set created according to the methods ofthe invention may identify genomic regions that are highly likely toinclude identifiable mutations in tumor sequences. This selector set mayinclude a relatively small total number of genomic regions and thus arelatively short cumulative length of genomic regions and yet mayprovide a high overall coverage of likely mutations in a population. Theselector set does not, therefore, need to be optimized on apatient-by-patient basis. The relatively short cumulative length ofgenomic regions also means that the analysis of cancer-derived cell-freeDNA using these libraries may be highly sensitive. The relatively shortcumulative length of genomic regions may allow the sequencing ofcell-free DNA to a great depth.

The selector sets comprising recurrently mutated genomic regions createdaccording to the instant methods may enable the identification ofpatient-specific mutations and/or tumor-specific mutations within thegenomic regions in a high percentage of subjects. Specifically, in theseselector sets, at least one mutation within the plurality of genomicregions may be present in at least 60% of a population of subjects withthe specific cancer. In some embodiments, at least two mutations withinthe plurality of genomic regions are present in at least 60% of apopulation of subjects with the specific cancer. In specificembodiments, at least three mutations, or even more, within theplurality of genomic regions are present in at least 60% of a populationof subjects with the specific cancer.

The methods for creating a selector set, as disclosed herein, may beimplemented by a programmed computer system. Therefore, according toanother aspect, the instant disclosure provides computer systems forcreating a selector set (e.g., library of recurrently mutated genomicregions). Such systems may comprise at least one processor and anon-transitory computer-readable medium storing computer-executableinstructions that, when executed by the at least one processor, causethe computer system to carry out the methods described herein forcreating a selector set (e.g., library).

ctDNA Detection Index

The methods, kits and systems disclosed herein may comprise a ctDNAdetection index or use thereof. Generally, the ctDNA detection index isbased on a p-value of one or more types of mutations present in a samplefrom a subject. The ctDNA detection index may comprise an integration ofinformation content across a plurality of mutations and classes ofsomatic mutations. The ctDNA detection index may be analogous to a falsepositive rate. The ctDNA detection index may be based on a decision treein which fusion breakpoints take precedence due to their nonexistentbackground and/or in which p-values from multiple classes of mutationsmay be integrated. The classes of mutations may include, but are notlimited to, SNVs, indels, copy number variants, and rearrangements.

The ctDNA detection index may be used to assess the statisticalsignificance of a selector set comprising genomic regions comprisingmultiple classes of mutations. For example, the ctDNA detection indexmay be used to assess the statistical significance of a selector setcomprising genomic regions comprising SNVs and indels. In anotherexample, the ctDNA detection index may be used to assess the statisticalsignificance of a selector set comprising genomic regions comprisingSNVs and rearrangements. In another example, the ctDNA detection indexmay be used to assess the statistical significance of a selector setcomprising genomic regions comprising rearrangements and indels. Inanother example, the ctDNA detection index may be used to assess thestatistical significance of a selector set comprising genomic regionscomprising SNVs, indels, copy number variants, and rearrangements. Thecalculation of the ctDNA detection index may be based on the types(e.g., classes) of mutations within the genomic region of a selector setthat are detected in a subject. For example, a selector set may comprisegenomic regions comprising SNVs, indels, copy number variants, andrearrangements, however, the types of mutations for the selector thatare detected in a subject may be SNVs and indels. The ctDNA detectionindex may be determined by combining a p-value of the SNVs and a p-valueof the indels. Any method that is suitable for combining independent,partial tests may be used to combine the p-value of the SNVs and indels.Combining the p-values of the SNVs and indels may be based on Fisher'smethod.

A method of determining a ctDNA detection index may comprise (a)detecting a presence of one or more mutations in one or more samplesfrom a subject, wherein the one or more mutations are based on aselector set comprising genomic regions comprising the one or moremutations; (b) determining a mutation type of the one or more mutationspresent in the sample; and (c) calculating a ctDNA detection index basedon a p-value of the mutation type of mutations present in the one ormore samples.

For instances in which a single type of mutation is present in thesample from the subject, the ctDNA detection index is based on thep-value of the single type of mutation. The p-value of the single typeof mutation may be estimated by Monte Carlo sampling. Monte Carlosampling may use a broad class of computational algorithms that rely onrepeated random sampling to obtain a p-value. The ctDNA detection indexmay be equivalent to the p-value of the single type of mutation.

For instances in which a rearrangement (e.g., fusion) is detected in atumor sample and a plasma sample from the subject, the ctDNA detectionindex is based on the p-value of the rearrangement. The p-value of therearrangement may be 0. Thus, the ctDNA detection index is the p-valueof the rearrangement, which is 0.

For instances in which a rearrangement (e.g., fusion) is detected inonly a tumor sample from the subject and not in a plasma sample from thesubject, the ctDNA detection index is based on the p-value of the othertypes of mutations.

For instances in which (a) a SNV and indel are detected in a sample fromthe subject; (b) a p-value of the SNV is less than 0.1 and a p-value ofthe indel is less than 0.1; and (c) a rearrangement is not detected in aplasma sample from the subject, the ctDNA detection index is calculatedbased on the combined p-values of the SNV and indel. Any method that issuitable for combining independent, partial tests may be used to combinethe p-value of the SNVs and indels. The p-values of the SNV and indelmay be combined according to Fisher's method. Thus, the ctDNA detectionindex is the combined p-value of the SNV and indel.

For instances in which (a) a SNV and indel are detected in a sample fromthe subject; (b) a p-value of the SNV is not less than 0.1 or a p-valueof the indel is not less than 0.1; and (c) a rearrangement is notdetected in a plasma sample from the subject, the ctDNA detection indexis based on the p-value of the SNV. Thus, the ctDNA detection index isthe p-value of the SNV.

A ctDNA detection index may be significant if the ctDNA detection indexis less than or equal to 0.10, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03,0.02, or 0.01. A ctDNA detection index may be significant if the ctDNAdetection index is less than or equal to 0.05. A ctDNA detection indexmay be significant if the ctDNA detection index is less than or equal toa false positive rate (FPR).

A ctDNA detection index may be calculated for a subject based on his orher array of reporters (e.g., mutations) using the following rules,executed in any order:

-   -   (i) For cases where only a single reporter type is present in a        patient's tumor, the corresponding p-value is used (estimated by        Monte Carlo sampling).    -   (ii) If SNV and indel reporters are detected, and if each        independently has a p-value <0.1, their respective p-values are        combined using Fisher's method. Otherwise, given the        prioritization of SNVs in the selector design, the SNV p-value        is used.    -   (iii) If a fusion breakpoint identified in a tumor sample (e.g.,        involving ROS1, ALK, or RET) is recovered in plasma DNA from the        same patient, it trumps all other mutation types, and its        p-value (˜0) is used.    -   (iv) If a fusion detected in the tumor is not found in        corresponding plasma (potentially due to hybridization        inefficiency), the p-value for any remaining mutation type(s) is        used.

The ctDNA detection index may be considered significant if the ctDNAdetection index is ≦0.05 (≈false positive rate (FPR)≦5%), which is thethreshold that maximized CAPP-Seq sensitivity and specificity in ROCanalyses (determined by Euclidean distance to a perfect classifier;e.g., true positive report (TPR)=1 and FPR=0).

Calculating a ctDNA detection index may comprise determining asignificance of SNVs. In some embodiments, to evaluate the significanceSNVs, the strategy integrates cfDNA fractions across all somatic SNVs,performs a position-specific background adjustment, and evaluatesstatistical significance by Monte Carlo sampling of background allelesacross the selector. This allows the quantitation of low levels of ctDNAwith potentially high rates of allelic drop out. The method forevaluating the significance of SNVs may utilize the following steps:

-   -   adjusting the allelic fraction f for each of n SNVs from patient        P for a given cfDNA sample θ by the operation f*=max{0,        f−(e−μ)}, where f is the raw allelic fraction in cfDNA, e is the        position-specific error rate for the given allele across all        cfDNA samples, and μ denotes the mean selector-wide background        rate;    -   comparing with Monte Carlo simulation the adjusted mean SNV        fraction F*(=(Σf*)/n) against the null distribution of        background alleles across the selector;    -   determining a SNV p-value for patient P as the percentile of F*        with respect to the null distribution of background alleles in        θ.

Calculating a ctDNA detection index may comprise determining asignificance of rearrangements. The recovery of a tumor-derived genomicfusion (rearrangement) can be assigned a p-value of ˜0, due to the verylow error rate.

Calculating a ctDNA detection index may comprise determining asignificance of indels. The analysis of insertions and deletions(indels) may be separately evaluated utilizing the following steps:

-   -   For each indel in patient P compare its fraction in a given        cfDNA sample θ against its fraction in every cfDNA sample in a        cohort (excluding cfDNA samples from the same patient P) with a        Z-test; where each read strand is optionally assessed separately        and combined into a single Z-score;    -   if patient P has more than 1 indel, all indel-specific Z-scores        are combined into a final Z statistic.

The p-values of the different mutation types may be integrated toestimate the statistical significance (e.g., p-value) of tumor burdenquantitation. Thus, the ctDNA detection index, which integrates thep-values of different mutation types, may be used to estimate thestatistical significance of tumor burden quantitation. For each sample,a ctDNA detection index may be calculated based on p-value integrationfrom the plurality of somatic mutations that are detected. The ctDNAdetection index may be determined based on the methods disclosed herein.For cases where only a single somatic mutation is present in a sample,the corresponding p-value may be used. If a fusion breakpoint identifiedin a tumor sample is recovered in cfDNA from the same patient, thep-value of the fusion breakpoint may be used. If SNV and indel somaticmutations are detected, and if each independently has a p-value <0.1,their respective p-values may be combined and the resulting p-value isused. If the ctDNA detection index is determined to be 0.05, then thep-value of the tumor burden quantitation is 0.05. A ctDNA detectionindex of ≦0.05 may suggest that a subject's mutations are significantlydetectable in a sample from the subject. A ctDNA detection index that isless than the false positive rate (FPR) may suggest that a subject'smutations are significantly detectable in a sample from the subject.

Selector Set Sensitivity and Specificity

The selector set may be chosen to provide a desired sensitivity and/orspecificity. As is known in the art, the relative sensitivity and/orspecificity of a predictive model can be “tuned” to favor either theselectivity metric or the sensitivity metric, where the two metrics havean inverse relationship. One or both of sensitivity and specificity canbe at least about at least about 0.6, at least about 0.65, at leastabout 0.7, at least about 0.75, at least about 0.8, at least about 0.85,at least about 0.9, or higher.

The sensitivity and specificity may be statistical measures of theperformance of selector set to perform a function. For example, thesensitivity of the selector set may be used to assess the use of theselector set to correctly diagnose or prognosticate a status or outcomeof a cancer in a subject. The sensitivity of the selector set maymeasure the proportion of subjects which are correctly identified assuffering from a cancer. The sensitivity of the selector set may alsomeasure the use of the selector set to correctly screen for a cancer ina subject. The sensitivity of the selector set may also measure the useof the selector set to correctly diagnose a cancer in a subject. Thesensitivity of the selector set may also measure the use of the selectorset to correctly prognosticate a cancer in a subject. The sensitivity ofthe selector set may also measure the use of the selector set tocorrectly identify a subject as a responder to a therapeutic regimen.The sensitivity may be at least about 60%, 61%, 62%, 63%, 64%, 65%, 66%,67%, 68%, 69%, 70% or greater. The sensitivity may be at least about72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or greater.

Sensitivity may vary according to the tumor stage. The sensitivity maybe at least about 50%, at least about 52%, at least about 55%, at leastabout 57%, at least about 60%, at least about 62%, at least about 65%,at least about 67%, at least about 70%, at least about 72%, at leastabout 75%, at least about 77%, at least about 80%, at least about 85%,at least about 87%, at least about 90%, at least about 92%, at leastabout 95%, at least about 98%, at least about 99% or more for tumors atstage I. The sensitivity may be at least about 50% for tumors at stageI. The sensitivity may be at least about 65% for tumors at stage I. Thesensitivity may be at least about 72% for tumors at stage I. Thesensitivity may be at least about 75% for tumors at stage I Thesensitivity may be at least about 85% for tumors at stage I Thesensitivity may be at least about 92% for tumors at stage I.

The sensitivity may be at least about 50%, at least about 52%, at leastabout 55%, at least about 57%, at least about 60%, at least about 62%,at least about 65%, at least about 67%, at least about 70%, at leastabout 72%, at least about 75%, at least about 77%, at least about 80%,at least about 85%, at least about 87%, at least about 90%, at leastabout 92%, at least about 95%, at least about 98%, at least about 99% ormore for tumors at stage II. The sensitivity may be at least about 60%for tumors at stage II. The sensitivity may be at least about 75% fortumors at stage II. The sensitivity may be at least about 85% for tumorsat stage II. The sensitivity may be at least about 92% for tumors atstage II.

The sensitivity may be at least about 50%, at least about 52%, at leastabout 55%, at least about 57%, at least about 60%, at least about 62%,at least about 65%, at least about 67%, at least about 70%, at leastabout 72%, at least about 75%, at least about 77%, at least about 80%,at least about 85%, at least about 87%, at least about 90%, at leastabout 92%, at least about 95%, at least about 98%, at least about 99% ormore for tumors at stage III. The sensitivity may be at least about 60%for tumors at stage III. The sensitivity may be at least about 75% fortumors at stage III. The sensitivity may be at least about 85% fortumors at stage III. The sensitivity may be at least about 92% fortumors at stage III.

The sensitivity may be at least about 50%, at least about 52%, at leastabout 55%, at least about 57%, at least about 60%, at least about 62%,at least about 65%, at least about 67%, at least about 70%, at leastabout 72%, at least about 75%, at least about 77%, at least about 80%,at least about 85%, at least about 87%, at least about 90%, at leastabout 92%, at least about 95%, at least about 98%, at least about 99% ormore for tumors at stage IV. The sensitivity may be at least about 60%for tumors at stage IV. The sensitivity may be at least about 75% fortumors at stage IV. The sensitivity may be at least about 85% for tumorsat stage IV. The sensitivity may be at least about 92% for tumors atstage IV.

The sensitivity may be at least about and may be at least about 60%, atleast about 65%, at least about 70%, at least about 75%, at least about80%, at least about 85%, at least about 87%, at least about 90%, atleast about 92%, at least about 95%, at least about 98%, at least about99% or more with healthy controls.

The AUC value may also vary according to tumor stage. The AUC value maybe at least about 0.50, at least about 0.52, at least about 0.55, atleast about 0.57, at least about 0.60, at least about 0.62, at leastabout 0.65, at least about 0.67, at least about 0.70, at least about0.72, at least about 0.75, at least about 0.77, at least about 0.80, atleast about 0.82, at least about 0.85, at least about 0.87, at leastabout 0.90, at least about 0.92, at least about 0.95, at least about0.97 or more for stage I cancer. The AUC value may be at least about0.50 for stage I cancer. The AUC value may be at least about 0.55 forstage I cancer. The AUC value may be at least about 0.60 for stage Icancer. The AUC value may be at least about 0.70 for stage I cancer. TheAUC value may be at least about 0.75 for stage I cancer. The AUC valuemay be at least about 0.80 for stage I cancer.

The AUC value may be at least about 0.50, at least about 0.52, at leastabout 0.55, at least about 0.57, at least about 0.60, at least about0.62, at least about 0.65, at least about 0.67, at least about 0.70, atleast about 0.72, at least about 0.75, at least about 0.77, at leastabout 0.80, at least about 0.82, at least about 0.85, at least about0.87, at least about 0.90, at least about 0.92, at least about 0.95, atleast about 0.97 or more for stage II cancer. The AUC value may be atleast about 0.50 for stage II cancer. The AUC value may be at leastabout 0.55 for stage II cancer. The AUC value may be at least about 0.60for stage II cancer. The AUC value may be at least about 0.70 for stageII cancer. The AUC value may be at least about 0.75 for stage II cancer.The AUC value may be at least about 0.80 for stage II cancer. The AUCvalue may be at least about 0.90 for stage II cancer. The AUC value maybe at least about 0.95 for stage II cancer.

The AUC value may be at least about 0.50, at least about 0.52, at leastabout 0.55, at least about 0.57, at least about 0.60, at least about0.62, at least about 0.65, at least about 0.67, at least about 0.70, atleast about 0.72, at least about 0.75, at least about 0.77, at leastabout 0.80, at least about 0.82, at least about 0.85, at least about0.87, at least about 0.90, at least about 0.92, at least about 0.95, atleast about 0.97 or more for stage III cancer. The AUC value may be atleast about 0.50 for stage III cancer. The AUC value may be at leastabout 0.55 for stage III cancer. The AUC value may be at least about0.60 for stage III cancer. The AUC value may be at least about 0.70 forstage III cancer. The AUC value may be at least about 0.75 for stage IIIcancer. The AUC value may be at least about 0.80 for stage III cancer.The AUC value may be at least about 0.90 for stage III cancer. The AUCvalue may be at least about 0.95 for stage III cancer.

The AUC value may be at least about 0.50, at least about 0.52, at leastabout 0.55, at least about 0.57, at least about 0.60, at least about0.62, at least about 0.65, at least about 0.67, at least about 0.70, atleast about 0.72, at least about 0.75, at least about 0.77, at leastabout 0.80, at least about 0.82, at least about 0.85, at least about0.87, at least about 0.90, at least about 0.92, at least about 0.95, atleast about 0.97 or more for stage IV cancer. The AUC value may be atleast about 0.50 for stage IV cancer. The AUC value may be at leastabout 0.55 for stage IV cancer. The AUC value may be at least about 0.60for stage IV cancer. The AUC value may be at least about 0.70 for stageIV cancer. The AUC value may be at least about 0.75 for stage IV cancer.The AUC value may be at least about 0.80 for stage IV cancer. The AUCvalue may be at least about 0.90 for stage IV cancer. The AUC value maybe at least about 0.95 for stage IV cancer.

The AUC values may be at least about 0.70, at least about 0.75, at leastabout 0.80, at least about 0.85, at least about 0.90, at least about0.95 for healthy controls.

The specificity of the selector may measure the proportion of subjectswhich are correctly identified as not suffering from a cancer. Thespecificity of the selector set may also measure the use of the selectorset to correctly make a diagnosis of no cancer in a subject. Thespecificity of the selector set may also measure the use of the selectorset to correctly identify a subject as a non-responder to a therapeuticregimen. The specificity may be at least about 60%, 61%, 62%, 63%, 64%,65%, 66%, 67%, 68%, 69%, 70% or greater. The specificity may be at leastabout 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or greater.

The selector set may be used to detect, diagnose, and/or prognosticate astatus or outcome of a cancer in a subject based on the detection of oneor more mutations within one or more genomic regions in the selector setin a sample from the subject. The sensitivity and/or specificity of theselector set to detect, diagnose, and/or prognosticate the status oroutcome of the cancer in the subject may be tuned (e.g.,adjusted/modified) by the ctDNA detection index. The ctDNA detectionindex may be used to assess the significance of classes of mutationsdetected in the sample from the subject by the selector set. The ctDNAdetection index may be used to determine whether the detection of one ormore classes of mutations by the selector set is significant. Forexample, the ctDNA detection index may determine that the classes ofmutations detected by the selector set in a first subject isstatistically significant, which may result in a diagnosis of cancer inthe first subject. The ctDNA detection index may determine that theclasses of mutations detected by the selector set in a second subject isnot statistically significant, which may result in a diagnosis of nocancer in the second subject. As such, the ctDNA detection index mayaffect the analysis of the specificity and/or sensitivity of theselector set to detect, diagnose, and/or prognosticate the status oroutcome of the cancer in the subject.

Identification of Rearrangements

Further disclosed herein are methods of identifying rearrangements. Therearrangement may be a genomic fusion event and/or breakpoint. Themethod may be used for de novo analysis of cfDNA samples. Alternatively,the method may be used for analysis of known tumor/germline DNA samples.The method may comprise a heuristic approach. Generally, the method maycomprise (a) obtaining an alignment file of pair-end reads, exoncoordinates, a reference genome, or a combination thereof; and (b)applying an algorithm to information from the alignment file to identifyone or more rearrangements. The algorithm may be applied to informationpertaining to one or more genomic regions. The algorithm may be appliedto information that overlaps with one or more genomic regions.

The method may be termed FACTERA (FACile Translocation Enumeration andRecovery Algorithm). As input, FACTERA may use an alignment file ofpaired-end reads, exon coordinates, and a reference genome. In addition,the analysis can be optionally restricted to reads that overlapparticular genomic regions. FACTERA may process the input in threesequential phases: identification of discordant reads, detection ofbreakpoints at base pair-resolution, and in silico validation ofcandidate fusions.

Further disclosed herein is a method of identifying rearrangementscomprising (a) obtaining sequencing information pertaining to aplurality of genomic regions; (b) producing a list of genomic regionsadjacent to one or more candidate rearrangement sites; (c) applying analgorithm to validate candidate rearrangement sites, thereby identifyingrearrangements.

The sequencing information may comprise an alignment file. The alignmentfile may comprise an alignment file of pair-end reads, exon coordinates,and a reference genome. The sequencing information may be obtained froma database. The database may comprise sequencing information pertainingto a population of subjects suffering from a disease or condition. Thedatabase may be a pharmacogenomics database. The sequencing informationmay be obtained from one or more samples from one or more subjects.

Producing the list of genomic regions adjacent to the one or morecandidate rearrangement sites may comprise identifying discordant readpairs based on the sequencing information. A discordant read-pair mayrefer to a read and its mate, where the insert size is not equal to(e.g., greater or less than) the expected distribution of the dataset,or where the mapping orientation of the reads is unexpected (e.g. bothon the same strand). Producing the list of genomic regions adjacent tothe one or more candidate rearrangement sites may comprise classifyingthe discordant read pairs based on the sequencing information.

Discordant read pairs may be introduced by NGS library preparationand/or sequencing artifacts (e.g., jumping PCR). However, they are alsolikely to flank the breakpoints of bona fide fusion events. Producing alist of genomic regions adjacent to the one or more candidaterearrangement sites may further comprise ranking the genomic regions.The genomic regions may be ranked in decreasing order of discordant readdepth. The method may further comprise eliminating duplicate fragments.Producing a list of genomic regions adjacent to the one or morecandidate rearrangement sites may comprise selecting genomic regionswith a minimum user-defined read depth. The read depth may be at least2×, 3×, 4×, 5×, 6×, 7×, 8×, 9×, 10× or more. The read depth may be atleast about 2×.

Producing the list of genomic regions adjacent to the one or morecandidate fusion sites may comprise use of one or more algorithms. Thealgorithm may analyze properly paired reads in which one of the tworeads is “soft-clipped,” or truncated. Soft-clipping may refer totruncating one or more ends of the paired reads. Soft-clipping maytruncate the one or more ends by removing less than or equal to 10, 9,8, 7, 6, 5, 4, 3, 2, 1 base or base pair from the paired reads.Soft-clipping may comprise removing at least one base or base pair fromthe paired reads. Soft-clipping may comprise removing at least one baseor base pair from one end of the paired reads. Soft-clipping maycomprise removing at least one base or base pair from both ends of thepaired reads. Soft-clipped reads may allow for precise breakpointdetermination. The precise breakpoint may be identified by parsing theCIGAR string associated with each mapped read, which compactly specifiesthe alignment operation used on each base (e.g. My=y contiguous baseswere mapped, Sx=x bases were skipped). The algorithm may analyzesoft-clipped reads with a specific pattern. For example, the algorithmmay analyze soft-clipped reads with the following patterns, SxMy orMySx. The number of skipped bases x may have a minimum requirement. Bysetting a minimum requirement for the number of skipped bases x, theimpact of non-specific sequence alignments may be reduced. The number ofskipped bases may be at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25 or more. The number of skipped bases may be atleast 16. The number of skipped bases may be user-defined. The number ofcontiguous bases y may also be used-defined.

An algorithm may be used to validate candidate rearrangement sites. Thealgorithm may determine the read frequency for the candidaterearrangement sites. The algorithm may eliminate candidate rearrangementsites that do not meet a minimum read frequency. The minimum readfrequency may be user-defined. The minimum read frequency may be atleast about 2, 3, 4, 5, 6, 7, 8, 9, 10 or more reads. The minimum readfrequency may be at least about 2 reads. The algorithm may rank thecandidate rearrangement sites based on the read frequency. A candidaterearrangement site may contain multiple soft-clipped reads. Thealgorithm may select a representative soft-clipped read for a candidaterearrangement site. Selection of the representative soft-clipped readmay be based on selecting a soft-clipped read that has a length that isclosest to half the read length. If the mapped region of therepresentative soft-clipped read matches the mapped region of anothersoft-clipped read of the candidate rearrangement site, the algorithm mayannotate the candidate rearrangement site as a rearrangement event. Ifthe mapped region of the representative soft-clipped read matches themapped region of another soft-clipped read of the candidaterearrangement site, the algorithm may identify the candidaterearrangement site as a rearrangement. If the mapped region of therepresentative soft-clipped read matches the mapped region of anothersoft-clipped read of the candidate rearrangement site, the algorithm mayannotate the candidate rearrangement site as a fusion event. Applyingthe algorithm to validate the candidate rearrangements may compriseidentifying the candidate rearrangement as a rearrangement if the two ormore reads have a sequence alignment.

Validating the candidate rearrangement sites may further comprise usingan algorithm to assess inter-read concordance. The algorithm may assessinter-read concordance by dividing a first sequence read of asoft-clipped sequence of a candidate rearrangement site into multiplepossible subsequences of a user-defined length k. A second sequence readof the soft-clipped sequence may be divided into subsequences of lengthk. Subsequences of size k of the second sequence read may be compared tothe first sequencing read, and the concordance of the two reads may bedetermined. For example, the soft-clipped sequence of a candidate fusionmay be 100 bases and the soft-clipped sequence may be subdivided into auser-defined length of 10 bases. The subsequences with a length of 10may be extracted from the first read and stored. A second read may becompared to the first read by selecting subsequences of 10 bases in thesecond read. The user-defined lengths may allow parts of the second readto be merged with the soft-clipped (e.g., non-mapping) parts of thefirst read into a composite sequence which is then assessed for improvedmapping properties. Validating the candidate rearrangement may comprisedividing a first read into subsequences of k-mers. A second read may bedivided into k-mers in order to rapidly compare it to the first read. Ifany k-mers overlap the first read, they are counted and used to assesssequence similarity. The two reads may be considered concordant if aminimum matching threshold is achieved. The minimum matching thresholdmay be a user-defined value. The minimum matching threshold may be 50%of the shortest length of the two sequences being compared. For example,the first sequence read may be 100 bases and the second sequence readmay be 130 bases. The minimum matching threshold may be 50 bases (e.g.,100 bases times 0.50). The minimum matching threshold may be at least10%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, or 80%of the shortest length of the two sequences being compared. Thealgorithm may process 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,1500, 2000 or more putative breakpoint pairs for each discordant gene(or genomic region) pair. The number of putative breakpoint pairs thatthe algorithm processes may be user-defined. Moreover, for a gene pair,the algorithm may compare reads whose orientations are compatible withvalid fusions. Such reads may have soft-clipped sequences facingopposite directions. When this condition is not satisfied, the algorithmmay use the reverse complement of read 1 for k-mer analysis.

In some instances, genomic subsequences flanking the true breakpoint maybe nearly or completely identical, causing the aligned portions ofsoft-clipped reads to overlap. This may prevent an unambiguousdetermination of the breakpoint. As such, an algorithm may be used toadjust the breakpoint in one read (e.g., read 2) to match the other(e.g., read 1). For a read, the algorithm may calculate the distancebetween the breakpoint and the read coordinate corresponding to thefirst k-mer match between reads. For example, let x be defined as thedistance between the breakpoint coordinate of read 1 and the index ofthe first matching k-mer, j, and y be defined as the correspondingdistance for read 2. Then, the offset is estimated as the difference indistances (x, y) between the two reads. Thus, for instances in which afusion event cannot be unambiguously determined based on the sequencereads, an algorithm is used to determine a fusion site.

The method may further comprise in silico validation of candidaterearrangement sites. An algorithm may perform a local realignment ofreads of the candidate rearrangement sites against a referencerearrangement sequence. The reference rearrangement sequence may beobtained from a reference genome. The local alignment may be ofsequences flanking the candidate rearrangement site. The local alignmentmay be of sequences within 100, 200, 300, 400, 500, 600, 700, 800, 900,or 1000 or more base pairs of the candidate rearrangement site. Thelocal alignment may be of sequences within 500 base pairs of thecandidate rearrangement site. BLAST may be used align the sequences. ABLAST database may be constructed by collecting reads that map to acandidate fusion sequence, including discordant reads and soft-clippedreads, as well as unmapped reads in the original input file. Reads thatmap to the reference rearrangement sequence with a user-defined identity(e.g., at least 95%) and/or a length of the aligned sequences is auser-defined percentage (e.g., 90%) of the input read length. The readsthat span or flank the breakpoint may be counted. The user-definedidentity may be at least about 70%, 75%, 80%, 85%, 90%, 95%, 97% ormore. The length of the aligned sequences may be at least about 70%,75%, 80%, 85%, 90%, or 95% or more of the input read length (e.g., readlength of the candidate rearrangement sequence). The output redundanciesmay be minimized by removing fusion sequences within an interval of atleast 20 base pairs or more of a fusion sequence with greater readsupport and with the same sequence orientation (to avoid removingreciprocal fusions).

The method may further comprise producing an output pertaining to therearrangement. The output may comprise one or more of the following genepair, genomic coordinates of the rearrangement, the orientation of therearrangement (e.g., forward-forward or forward-reverse), genomicsequences within 50 bp of the rearrangement, and depth statistics forreads spanning and flanking the rearrangement.

The method may further comprise enumerating a fusion allele frequency.For example, fusion allele frequency in sequenced cfDNA may beenumerated as described herein and in Example 1. The fusion allelefrequency may be calculated as α/β, where α is the number ofbreakpoint-spanning reads, and β is the mean overall depth within agenomic region at a predefined distance around the breakpoint. Thus, thefusion allele frequency may be calculated by dividing the number ofrearrangement-spanning reads by the mean overall depth within a genomicregion at a predefined distance around the breakpoint.

The method of identifying rearrangements may be applied to whole genomesequencing data or other suitable next-generation sequencing datasets.The genomic regions comprising the rearrangements identified from thisdata may be used to design a selector set.

The method of identifying rearrangements may be applied to sequencingdata from a subject. The method may identify subject-specificbreakpoints in tumor genomic DNA captured by a selector set. The methodmay be used to determine whether the subject-specific breakpoints arepresent in corresponding plasma DNA sample from the subject.

Identification of Tumor-Derived SNVs

Further disclosed herein are non-invasive methods of identifyingtumor-derived SNVs.

The tumor-derived SNVs may be identified without prior knowledge ofsomatic variants identified in a corresponding tumor biopsy sample. Insome embodiments of the invention, cfDNA is analyzed without comparisonto a known tumor DNA sample from the patient. In such embodiments, thepresence of ctDNA utilizes iterative models for (i) background noise inpaired germline DNA, (ii) base-pair resolution background frequencies incfDNA across the selector set, and (iii) sequencing error in cfDNA.These methods may utilize the following steps, which can be iteratedthrough data point to automatically call tumor-derived SNVs:

-   -   taking allele frequencies from a single cfDNA sample and        selecting high quality data;    -   testing whether a given input cfDNA allele is significantly        different from the corresponding paired germline allele;    -   assembling a database of cfDNA background allele frequencies;    -   testing whether a given input allele differs significantly from        cfDNA background at the same position, and selecting those with        an average background frequency of a predetermined threshold,        e.g. 5% or greater; 2.5% or greater, etc.    -   distinguishing tumor-derived SNVs from remaining background        noise by outlier analysis.

The non-invasive method of identifying tumor-derived SNVs may comprise(a) obtaining a sample from a subject suffering from a cancer orsuspected of suffering from a cancer; (b) conducting a sequencingreaction on the sample to produce sequencing information; (c) applyingan algorithm to the sequencing information to produce a list ofcandidate tumor alleles based on the sequencing information from step(b), wherein a candidate tumor allele comprises a non-dominant base thatis not a germline SNP; and (d) identifying tumor-derived SNVs based onthe list of candidate tumor alleles. The candidate tumor allele mayrefer to a genomic region comprising a candidate SNV.

The candidate tumor allele may be a high quality candidate tumor allele.A high quality background allele may refer to the non-dominant base withthe highest fractional abundance, excluding germline SNPs. Thefractional abundance of a candidate tumor allele may be calculated bydividing a number of supporting reads by a total sequencing depth atthat genomic position. For example, for a candidate mutation in a firstgenomic region, twenty sequence reads may contain a first sequence withthe candidate mutation and 100 sequence reads may contain a secondsequence without the candidate mutation. The candidate tumor allele maybe the first sequence containing the candidate mutation. Based on thisexample, the fractional abundance of the candidate tumor allele would be20 divided by 120, which is ˜17%. Producing the list of candidate tumoralleles may comprise ranking the tumor alleles based on their fractionalabundance. Producing the list of candidate tumor alleles may compriseselecting tumor alleles with the highest fractional abundance. Producingthe list of candidate tumor alleles may comprise selecting tumor alleleswith a fractional abundance in the top 70^(th), 75^(th), 80^(th),85^(th), 87^(th), 90^(th), 92^(nd), 95^(th), or 97^(th) percentile. Acandidate tumor allele may have a fractional abundance of less than 35%,30%, 27%, 25%, 20%, 18%, 15%, 13%, 10%, 9%, 8%, 7%, 6.5%, 6%, 5.5%, 5%,4.5%, 4%, 3.5%, 3%, 2.5%, 2%, 1.75%, 1.50%, 1.25%, or 1% of the totalalleles pertaining to the candidate tumor allele in the sample from thesubject. A candidate tumor allele may have a fractional abundance ofless than 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, or 0.1% ofthe total alleles pertaining to the candidate tumor allele in the samplefrom the subject. The candidate tumor allele may have a fractionalabundance of less than 0.5% of the total alleles in the sample from thesubject. The sample may comprise paired samples from the subject. Thus,the fractional abundance may be based on paired samples from thesubject. The paired samples may comprise a sample containing suspectedtumor-derived nucleic acids and a sample containing non-tumor-derivednucleic acids. For example, the paired samples may comprise a plasmasample and a sample containing peripheral blood lymphocytes (PBLs) orperipheral blood mononuclear cells (PBMCs).

The candidate tumor allele may have a minimum sequencing depth.Producing the list of candidate tumor alleles may comprise ranking thetumor alleles based on their sequencing depth. Producing the list ofcandidate tumor alleles may comprise selecting tumor alleles that meet aminimum sequencing depth. The minimum sequencing depth may be at least100×, 200×, 300×, 400×, 500×, 600×, 700×, 800×, 900×, 1000× or more. Theminimum sequencing depth may be at least about 500×. The minimumsequencing depth may be user-defined.

The candidate tumor allele may have a strand bias percentage. Producingthe list of candidate tumor alleles may comprise calculating the strandbias percentage of a tumor allele. Producing the list of candidate tumoralleles may comprise ranking the tumor alleles based on their strandbias percentage. Producing the list of candidate tumor alleles maycomprise selecting tumor alleles with a strand bias percentage of lessthan or equal to 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97%.Producing the list of candidate tumor alleles may comprise selectingtumor alleles with a strand bias percentage of less than or equal to90%. The strand bias percentage may be user-defined.

Producing the list of candidate tumor alleles may comprise comparing thesequence of the tumor allele to a reference tumor allele. The referencetumor allele may be a germline allele. Producing the list of candidatetumor alleles may comprise determining whether the candidate tumorallele is different from a reference tumor allele. Producing the list ofcandidate tumor alleles may comprise selecting tumor alleles that aredifferent from the reference tumor allele.

Determining whether the tumor allele is different from the referencetumor allele may comprise use of one or more statistical analyses. Thestatistical analysis may comprise using Bonferroni correction tocalculate a Bonferroni-adjusted binomial probability for a tumor allele.The Bonferroni-adjusted binomial probability may be calculated bydividing a desired p-value cutoff (alpha) by the number of hypothesestested. The number of hypotheses tested may be calculated by multiplyingthe number of bases in a selector by the number of possible basechanges. The Bonferroni-adjusted binomial probability may be calculatedby dividing the desired p-value cutoff (alpha) by the number of bases ina selector multiplied by the number of possible base changes. TheBonferroni-adjusted binomial probability may be used to determinewhether the tumor allele occurred by chance. Producing the list ofcandidate tumor alleles may comprise selecting tumor alleles based onthe Bonferroni-adjusted binomial probability. A candidate tumor allelemay have a Bonferroni-adjusted binomial probability of less than orequal to 3×10⁻⁸, 2.9×10⁻⁸, 2.8×10⁻⁸, 2.7×10⁻⁸, 2.6×10⁻⁸, 2.5×10⁻⁸,2.3×10⁻⁸, 2.2×10⁻⁸, 2.1×10⁻⁸, 2.09×10⁻⁸, 2.08×10⁻⁸, 2.07×10⁻⁸,2.06×10⁻⁸, 2.05×10⁻⁸, 2.04×10⁻⁸, 2.03×10⁻⁸, 2.02×10⁻⁸, 2.01×10⁻⁸ or2×10⁻⁸. A candidate tumor allele may have a Bonferroni-adjusted binomialprobability of less than or equal to 2.08×10⁻⁸.

Determining whether the tumor allele is different from the referencetumor allele may comprise use of a binomial distribution. The binomialdistribution may be used to assemble a database of candidate tumorallele frequencies. An algorithm, such as a Z-test, may be used todetermine whether a candidate tumor allele differs significantly from atypical circulating allele at the same position. A significantdifference may refer to a difference that is unlikely to have occurredby chance. The Z-test may be applied to the Bonferroni-adjustedbionomial probability of the tumor alleles to produce aBonferroni-adjusted single-tailed Z-score. The Bonferroni-adjustedsingle-tailed Z-score may be determined by using a normal distribution.A tumor allele with a Bonferroni-adjusted single-tailed Z-score ofgreater than or equal to 6, 5.9, 5.8, 5.7, 5.6, 5.5, 5.4, 5.3, 5.2, 5.1,or 5.0 is considered to be different from the reference tumor allele.Producing the list of candidate tumor alleles may comprise selectingtumor alleles with a Bonferroni-adjusted single-tailed Z-score ofgreater than or equal to 6, 5.9, 5.8, 5.7, 5.6, 5.5, 5.4, 5.3, 5.2, 5.1,or 5.0. Producing the list of candidate tumor alleles may compriseselecting tumor alleles with a Bonferroni-adjusted single-tailed Z-scoreof greater than 5.6.

Candidate tumor alleles may be based on genomic regions from a selectorset. The list of candidate tumor alleles may comprise candidate tumoralleles with a frequency of less than or equal to 10%, 9%, 8%, 7%, 6.5%,6%, 5.5%, 5%, 4.5%, 4%, 3.5%, or 3%. The list of candidate tumor allelesmay comprise candidate tumor alleles with a frequency of less than 5%.

Identifying tumor-derived SNVs based on the list of candidate tumoralleles may comprise testing the candidate tumor alleles from the listof candidate tumor alleles for sequencing errors. Testing the candidatetumor alleles for sequencing errors may be based on the duplication rateof the candidate tumor allele. The duplication rate may be determined bycomparing the number of supporting reads for a candidate tumor allelefor nondeduped data (e.g., all fragments meeting quality controlcriteria) and deduped data (e.g., unique fragments meeting qualitycontrol criteria). The candidate tumor alleles may be ranked based ontheir duplication rate. A tumor-derived SNV may be in a candidate tumorallele with a low duplication rate.

Identifying tumor-derived SNVs may further comprise use of an outlieranalysis. The outlier analysis may be used to distinguish candidatetumor-derived SNVs from the remaining background noise. The outlieranalysis may comprise comparing the square root of the robust distanceRd (Mahalanobis distance) to the square root of the quantiles of achi-squared distribution Cs. Tumor-derived SNVs may be identified fromthe outliers in the outlier analysis.

The sequencing information may pertain to regions flanking one or moregenomic regions from a selector set. The sequencing information maypertain to regions flanking genomic coordinates from a selector set. Thesequencing information may pertain to regions within 100, 200, 300, 400,500, 600, 700, 800, 900, 1000 or more base pairs of a genomic regionfrom a selector set. The sequencing information may pertain to regionswithin 500 base pairs of a genomic region from a selector set. Thesequencing information may pertain to regions within 100, 200, 300, 400,500, 600, 700, 800, 900, 1000 or more base pairs of a genomic coordinatefrom a selector set. The sequencing information may pertain to regionswithin 500 base pairs of a genomic coordinate from a selector set.

Computer Program

The methods described herein may be performed by a computer programproduct that comprises a computer executable logic that is recorded on acomputer readable medium. For example, the computer program can executesome or all of the following functions: (i) controlling isolation ofnucleic acids from a sample, (ii) pre-amplifying nucleic acids from thesample or (iii) selecting, amplifying, sequencing or arraying specificregions in the sample, (iv) identifying and quantifying somaticmutations in a sample, (v) comparing data on somatic mutations detectedfrom the sample with a predetermined threshold, (vi) determining thetumor load based on the presence of somatic mutations in the cfDNA, and(vii) declaring an assessment of tumor load, residual disease, responseto therapy, or initial diagnosis. The computer program may calculate arecurrence index. The computer program may rank genomic regions by therecurrence index. The computer program may select one or more genomicregions based on the recurrence index. The computer program may producea selector set. The computer program may add genomic regions to theselector set. The computer program may maximize subject coverage of theselector set. The computer program may maximize a median number ofmutations per subject in a population. The computer program maycalculate a ctDNA detection index. The computer program may calculate ap-value of one or more types of mutations. The computer program mayidentify genomic regions comprising one or more mutations present in oneor more subjects suffering from a cancer. The computer program mayidentify novel mutations present in one or more subjects suffering froma cancer. The computer program may identify novel fusions present in oneor more subjects suffering from a cancer.

The computer executable logic can work in any computer that may be anyof a variety of types of general-purpose computers such as a personalcomputer, network server, workstation, or other computer platform now orlater developed. In some embodiments, a computer program product isdescribed comprising a computer usable medium having the computerexecutable logic (computer software program, including program code)stored therein. The computer executable logic can be executed by aprocessor, causing the processor to perform functions described herein.In other embodiments, some functions are implemented primarily inhardware using, for example, a hardware state machine. Implementation ofthe hardware state machine so as to perform the functions describedherein will be apparent to those skilled in the relevant arts.

The program can provide a method of evaluating the presence of tumorcells in an individual by accessing data that reflects the sequence ofthe selected cfDNA from the individual, and/or the quantitation of oneor more nucleic acids from the cfDNA in the circulation of theindividual. The one or more nucleic acids from the cfDNA in thecirculation to be quantified may be based on genomic regions or genomiccoordinates provided by a selector set.

In one embodiment, the computer executing the computer logic of theinvention may also include a digital input device such as a scanner. Thedigital input device can provide information on a nucleic acid, e.g.,polymorphism levels/quantity.

In some embodiments, the invention provides a computer readable mediumcomprising a set of instructions recorded thereon to cause a computer toperform the steps of (i) receiving data from one or more nucleic acidsdetected in a sample; and (ii) diagnosing or predicting tumor load,residual disease, response to therapy, or initial diagnosis based on thequantitation.

Sequencing

Genotyping ctDNA and/or detection, identification and/or quantitation ofthe ctDNA can utilize sequencing. Sequencing can be accomplished usinghigh-throughput systems. In some cases, high throughput sequencinggenerates at least 1,000, at least 5,000, at least 10,000, at least20,000, at least 30,000, at least 40,000, at least 50,000, at least100,000 or at least 500,000 sequence reads per hour; with each readbeing at least 50, at least 60, at least 70, at least 80, at least 90,at least 100, at least 120 or at least 150 bases per read. Sequencingcan be performed using nucleic acids described herein such as genomicDNA, cDNA derived from RNA transcripts or RNA as a template. Sequencingmay comprise massively parallel sequencing.

In some embodiments, high-throughput sequencing involves the use oftechnology available by Helicos BioSciences Corporation (Cambridge,Mass.) such as the Single Molecule Sequencing by Synthesis (SMSS)method. In some embodiments, high-throughput sequencing involves the useof technology available by 454 Lifesciences, Inc. (Branford, Conn.) suchas the Pico Titer Plate device which includes a fiber optic plate thattransmits chemiluminescent signal generated by the sequencing reactionto be recorded by a CCD camera in the instrument. This use of fiberoptics allows for the detection of a minimum of 20 million base pairs in4.5 hours.

In some embodiments, high-throughput sequencing is performed usingClonal Single Molecule Array (Solexa, Inc.) or sequencing-by-synthesis(SBS) utilizing reversible terminator chemistry. These technologies aredescribed in part in U.S. Pat. Nos. 6,969,488; 6,897,023; 6,833,246;6,787,308; and US Publication Application Nos. 200401061 30;20030064398; 20030022207; and Constans, A, The Scientist 2003,17(13):36.

In some embodiments, high-throughput sequencing of RNA or DNA can takeplace using AnyDot.chips (Genovoxx, Germany), which allows for themonitoring of biological processes (e.g., miRNA expression or allelevariability (SNP detection). In particular, the AnyDot-chips allow for10×-50× enhancement of nucleotide fluorescence signal detection. Otherhigh-throughput sequencing systems include those disclosed in Venter,J., et al. Science 16 Feb. 2001; Adams, M. et al, Science 24 Mar. 2000;and M. J, Levene, et al. Science 299:682-686, January 2003; as well asUS Publication Application No. 20030044781 and 2006/0078937. The growingof the nucleic acid strand and identifying the added nucleotide analogmay be repeated so that the nucleic acid strand is further extended andthe sequence of the target nucleic acid is determined.

The methods disclosed herein may comprise conducting a sequencingreaction based on one or more genomic regions from a selector set. Theselector set may comprise one or more genomic regions from Table 2. Asequencing reaction may be performed on 10, 20, 30, 40, 50, 60, 70, 80,90, 100 or more genomic regions from a selector set based on Table 2. Asequencing reaction may be performed on 5%, 10%, 15%, 20%, 25%, 30%,35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% ormore of the genomic regions from a selector set based on Table 2.

A sequencing reaction may be performed on a subset of genomic regionsfrom a selector set. A sequencing reaction may be performed on 10, 20,30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or moregenomic regions from a selector set. A sequencing reaction may beperformed on 325, 350, 375, 400, 425, 450, 475, 500 or more genomicregions from a selector set.

A sequencing reaction may be performed on all of the genomic regionsfrom a selector set. Alternatively, a sequencing reaction may beperformed on 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, 95% or more of the genomic regions from aselector set. A sequencing reaction may be performed on at least 10% ofthe genomic regions from a selector set. A sequencing reaction may beperformed on at least 30% of the genomic regions from a selector set. Asequencing reaction may be performed on at least 50% of the genomicregions from a selector set.

A sequencing reaction may be performed on less than 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%,95% of the genomic regions from a selector set. A sequencing reactionmay be performed on less than 10% of the genomic regions from a selectorset. A sequencing reaction may be performed on less than 30% of thegenomic regions from a selector set. A sequencing reaction may beperformed on less than 50% of the genomic regions from a selector set.

The methods disclosed herein may comprise obtaining sequencinginformation for one or more genomic regions from a selector set.Sequencing information may be obtained for 10, 20, 30, 40, 50, 60, 70,80, 90, 100 or more genomic regions from a selector set based on Table2. Sequencing information may be obtained for 5%, 10%, 15%, 20%, 25%,30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%or more of the genomic regions from a selector set based on Table 2.

Sequencing information may be obtained for a subset of genomic regionsfrom a selector set. Sequencing information may be obtained for 10, 20,30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or moregenomic regions from a selector set. Sequencing information may beobtained for 325, 350, 375, 400, 425, 450, 475, 500 or more genomicregions from a selector set.

Sequencing information may be obtained for all of the genomic regionsfrom a selector set. Alternatively, sequencing information may beobtained for 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%,65%, 70%, 75%, 80%, 85%, 90%, or 95% or more of the genomic regions froma selector set. Sequencing information may be obtained for at least 10%of the genomic regions from a selector set. Sequencing information maybe obtained for at least 30% of the genomic regions from a selector set.

Sequencing information may be obtained for less than 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or95% of the genomic regions from a selector set. Sequencing informationmay be obtained for less than 10% of the genomic regions from a selectorset. Sequencing information may be obtained for less than 30% of thegenomic regions from a selector set. Sequencing information may beobtained for less than 50% of the genomic regions from a selector set.Sequencing information may be obtained for less than 70% of the genomicregions from a selector set.

Amplification

The methods disclosed herein may comprise amplification of cell-free DNA(cfDNA) and/or of circulating tumor DNA (ctDNA). Amplification maycomprise PCR-based amplification. Alternatively, amplification maycomprise nonPCR-based amplification.

Amplification of cfDNA and/or ctDNA may comprise using beadamplification followed by fiber optics detection as described inMarguiles et al. “Genome sequencing in microfabricated high-densitypricolitre reactors”, Nature, doi: 10.1038/nature03959; and well as inUS Publication Application Nos. 200200 12930; 20030058629; 20030 100102; 20030 148344; 20040248 161; 200500795 10,20050 124022; and20060078909.

Amplification of the nucleic acid may comprise use of one or morepolymerases. The polymerase may be a DNA polymerase. The polymerase maybe a RNA polymerase. The polymerase may be a high fidelity polymerase.The polymerase may be KAPA HiFi DNA polymerase. The polymerase may bePhusion DNA polymerase.

Amplification may comprise 20 or fewer amplification cycles.Amplification may comprise 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10,or 9 or fewer amplification cycles. Amplification may comprise 18 orfewer amplification cycles. Amplification may comprise 16 or feweramplification cycles. Amplification may comprise 15 or feweramplification cycles.

Sample

The methods, kits, and systems disclosed herein may comprise one or moresamples or uses thereof. A “sample” may refer to any biological samplethat is isolated from a subject. A sample can include, withoutlimitation, an aliquot of body fluid, whole blood, platelets, serum,plasma, red blood cells, white blood cells or leucocytes, endothelialcells, tissue biopsies, synovial fluid, lymphatic fluid, ascites fluid,and interstitial or extracellular fluid. The term “sample” may alsoencompass the fluid in spaces between cells, including gingivalcrevicular fluid, bone marrow, cerebrospinal fluid (CSF), saliva,mucous, sputum, semen, sweat, urine, or any other bodily fluids. “Bloodsample” can refer to whole blood or any fraction thereof, includingblood cells, red blood cells, white blood cells or leucocytes,platelets, serum and plasma. The sample may be from a bodily fluid. Thesample may be a plasma sample. The sample may be a serum sample. Thesample may be a tumor sample. Samples can be obtained from a subject bymeans including but not limited to venipuncture, excretion, ejaculation,massage, biopsy, needle aspirate, lavage, scraping, surgical incision,or intervention or other means known in the art.

Samples useful for the methods of the invention may comprise cell-freeDNA (cfDNA), e.g., DNA in a sample that is not contained within a cell.Typically such DNA may be fragmented, and may be on average about 170nucleotides in length, which may coincide with the length of DNA arounda single nucleosome. cfDNA may generally be a heterogeneous mixture ofDNA from normal and tumor cells, and an initial sample of cfDNA maygenerally not be enriched for recurrently mutated regions of a cancercell genome. The terms ctDNA, cell-free tumor DNA or “circulating tumor”DNA may be used to refer to the fraction of cfDNA in a sample that isderived from a tumor. One of skill in the art will understand thatgermline sequences may not be distinguished between a tumor source and anormal cell source, but sequences containing somatic mutations have ahigh probability of being derived from tumor DNA. A sample may be acontrol germline DNA sample. A sample may be a known tumor DNA sample. Asample may be cfDNA obtained from an individual suspected of havingctDNA in the sample.

The methods disclosed herein may comprise obtaining one or more samplesfrom a subject. The one or more samples may be a tumor nucleic acidsample. Alternatively, or additionally, the one or more samples may be agenomic nucleic acid sample. It should be understood that the step ofobtaining a tumor nucleic acid sample and a genomic nucleic acid samplefrom a subject with a specific cancer may occur in a single step.Alternatively, the step of obtaining a tumor nucleic acid sample and agenomic nucleic acid sample from a subject with a specific cancer mayoccur in separate steps. For example, it may be possible to obtain asingle tissue sample from a patient, for example from a biopsy sample,which includes both tumor nucleic acids and genomic nucleic acids. It isalso within the scope of this step to obtain the tumor nucleic acidsample and the genomic nucleic acid sample from the subject in separatesamples, in separate tissues, or even at separate times.

The sample may comprise nucleic acids. The nucleic acids may becell-free nucleic acids. The nucleic acids may be circulating nucleicacids. The nucleic acids may be from a tumor. The nucleic acids may becirculating tumor DNA (ctDNA). The nucleic acids may be cell-free DNA(cfDNA). The nucleic acids may be genomic nucleic acids. The nucleicacids may be tumor nucleic acids.

The step of obtaining a tumor nucleic acid sample and a genomic nucleicacid sample from a subject with a specific cancer may also include theprocess of extracting a biological fluid or tissue sample from thesubject with the specific cancer. These particular steps are wellunderstood by those of ordinary skill in the medical arts, particularlyby those working in the medical laboratory arts.

The step of obtaining a tumor nucleic acid sample and a genomic nucleicacid sample from a subject with a specific cancer may additionallyinclude procedures to improve the yield or recovery of the nucleic acidsin the sample. For example, the step may include laboratory proceduresto separate the nucleic acids from other cellular components andcontaminants that may be present in the biological fluid or tissuesample. As noted, such steps may improve the yield and/or may facilitatethe sequencing reactions.

It should also be understood that the step of obtaining a tumor nucleicacid sample and a genomic nucleic acid sample from a subject with aspecific cancer may be performed by a commercial laboratory that doesnot even have direct contact with the subject. For example, thecommercial laboratory may obtain the nucleic acid samples from ahospital or other clinical facility where, for example, a biopsy orother procedure is performed to obtain tissue from a subject. Thecommercial laboratory may thus carry out all the steps of theinstantly-disclosed methods at the request of, or under the instructionsof, the facility where the subject is being treated or diagnosed.

A sample may be selected for DNA corresponding to regions of recurrentmutations, utilizing a selector set as described herein. In someembodiments, the selection process comprises the following method. DNAobtained from cellular sources may be fragmented to approximate the sizeof cfDNA, e.g. of from about 50 to about 1 KB in length. The DNA maythen be denatured, and hybridized to a population of selector set probescomprising a specific binding member, e.g. biotin, etc. The compositionof hybridized DNA may then be applied to a complementary binding member,e.g. avidin, streptavidin, an antibody specific for a tag, etc., and theunbound DNA washed free. The selected DNA population may then be washedfree of the unbound DNA.

The captured DNA may then be sequenced by any suitable protocol. In someembodiments, the captured DNA is amplified prior to sequencing, wherethe amplification primers may utilize primers or oligonucleotidessuitable for high throughput sequencing. The resulting product may be aset of DNA sequences enriched for sequences corresponding to regions ofthe genome that have recurrent mutations in the cancer of interest. Theremaining analysis may utilize bioinformatics methods, which can varywith the type of somatic mutation, e.g. SNV, SNV, fusion, etc.

Further disclosed herein are methods of preparing a next-generationsequencing (NGS) library. The method may comprise (a) attaching adaptorsto a plurality of nucleic acids to produce a plurality ofadaptor-modified nucleic acids; and (b) amplifying the plurality ofadaptor-modified nucleic acids, thereby producing a NGS library, whereinamplifying comprises 1 to 20 amplification cycles.

The methods disclosed herein may comprise attaching adaptors to nucleicacids. Attaching adaptors to nucleic acids may comprise ligatingadaptors to nucleic acids. Attaching adaptors to nucleic acids maycomprise hybridizing adaptors to nucleic acids. Attaching adaptors tonucleic acids may comprise primer extension.

The plurality of nucleic acids may be from a sample. Attaching theadaptors to the plurality of nucleic acids may comprise contacting thesample with the adaptors.

Attaching the adaptors to the nucleic acids may comprise incubating theadaptors and nucleic acids at a specific temperature or temperaturerange. Attaching the adaptors to the nucleic acids may compriseincubating the adaptors and nucleic acids at 20° C. Attaching theadaptors to the nucleic acids may comprise incubating the adaptors andnucleic acids at less 20° C. Attaching the adaptors to the nucleic acidsmay comprise incubating the adaptors and nucleic acids at 19° C., 18°C., 17° C., 16° C. or less. Alternatively, attaching the adaptors to thenucleic acids may comprise incubating the adaptors and nucleic acids atvarying temperatures. For example, attaching the adaptors to the nucleicacids may comprise temperature cycling. Attaching the adaptors to thenucleic acids may comprise may comprise incubating the nucleic acids andadaptors at a first temperature for a first period of time, followed byincubation at one or more additional temperatures for one or moreadditional periods of time. The one or more additional temperatures maybe greater than the first temperature or preceding temperature.Alternatively, or additionally, the one or more additional temperaturesmay be less than the first temperature or preceding temperature. Forexample, the nucleic acids and adaptors may be incubated at 10° C. for30 second, followed by incubation at 30° C. for 30 seconds. Thetemperature cycling of 10° C. for 30 seconds and 30° C. for 30 secondmay be repeated multiple times. For example, attaching the adaptors tothe nucleic acids by temperature cycling may comprise alternating thetemperature from 10° C. to 30° C. in 30 second increments for a totaltime period of 12 to 16 hours.

The adaptors and nucleic acids may be incubated at a specifiedtemperature or temperature range for a period of time. The adaptors andnucleic acid may be incubated at a specific temperature or temperaturerange for at least about 15 minutes. The adaptors and nucleic acid maybe incubated at a specific temperature or temperature range for at leastabout 30 minutes, 60 minutes, 90 minutes, 120 minutes or more. Theadaptors and nucleic acid may be incubated at a specific temperature ortemperature range for at least about 1 hour, 2 hours, 3 hours, 4 hours,5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours, 14hours, 16 hours, or more. The adaptors and nucleic acid may be incubatedat a specific temperature or temperature range for at least about 16hours.

The adaptors may be attached to the nucleic acid by incubating thenucleic acids and the adaptors at a temperature less than or equal to20° C. for at least about 20, 30, 40, 50, 60, 70, 80, 90, 100 or moreminutes. The adaptors may be attached to the nucleic acid by incubatingthe nucleic acids and the adaptors at a temperature less than or equalto 20, 19, 18, 17, 16° C. for at least about 1 hour. The adaptors may beattached to the nucleic acid by incubating the nucleic acids and theadaptors at a temperature less than or equal to 18° C. for at leastabout 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or morehours. The adaptors may be attached to the nucleic acid by incubatingthe nucleic acids and the adaptors at a temperature less than or equalto 20, 19, 18, 17, 16° C. for at least about 5 hours. The adaptors maybe attached to the nucleic acid by incubating the nucleic acids and theadaptors at a temperature less than or equal to 16° C. for at leastabout 5 hours.

Attaching the adaptors to the nucleic acids may comprise use of one ormore enzymes. The enzyme may be a ligase. The ligase may be a DNAligase. The DNA ligase may be a T4 DNA ligase, E. coli DNA ligase,mammalian ligase, or a combination thereof. The mammalian ligase may beDNA ligase I, DNA ligase III, or DNA ligase IV. The ligase may be athermostable ligase.

The adaptor may comprise a universal primer binding sequence. Theadaptor may comprise a primer sequence. The primer sequence may enablesequencing of the adaptor-modified nucleic acids. The primer sequencemay enable amplification of the adaptor-modified nucleic acids. Theadaptor may comprise a barcode. The barcode may enable differentiationof two or more molecules of the same molecular species. The barcode mayenable quantification of one or more molecules.

The method may further comprise contacting the plurality of nucleicacids with a plurality of beads to produce a plurality ofbead-conjugated nucleic acids. The plurality of nucleic acids may becontacted with the plurality of beads after attaching the adaptors tothe nucleic acids. Alternatively, or additionally, the plurality ofnucleic acids may be contacted with the plurality of beads beforeamplification of the adaptor-modified nucleic acids. Alternatively, oradditionally, the plurality of nucleic acids may be contacted with theplurality of beads after amplification of the adaptor-modified nucleicacids.

The beads may be magnetic beads. The beads may be coated beads. Thebeads may be antibody-coated beads. The beads may be protein-coatedbeads. The beads may be coated with one or more functional groups. Thebeads may be coated with one or more oligonucleotides.

Amplifying the plurality of adaptor-modified nucleic acids may compriseany method known in the art. For example, amplifying may comprisePCR-based amplification. Alternatively, amplifying may comprisenonPCR-based amplification. Amplifying may comprise any of theamplification methods disclosed herein.

Amplifying the plurality of adaptor-modified nucleic acids may compriseamplifying a product or derivative of the adaptor-modified nucleicacids. A product or derivative of the adaptor-ligated nucleic acids maycomprise bead-conjugated nucleic acids, enriched-nucleic acids,fragmented nucleic acids, end-repaired nucleic acids, A-tailed nucleicacids, barcoded nucleic acids, or a combination thereof

Amplifying the adaptor-modified nucleic acids may comprise 1 to 20amplification cycles. Amplifying the adaptor-modified nucleic acids maycomprise 1 to 18 amplification cycles. Amplifying the adaptor-modifiednucleic acids may comprise 1 to 17 amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 1 to 16 amplificationcycles. Amplifying the adaptor-modified nucleic acids may comprise 2 to20 amplification cycles. Amplifying the adaptor-modified nucleic acidsmay comprise 2 to 18 amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 2 to 16 amplificationcycles. Amplifying the adaptor-modified nucleic acids may comprise 3 to20 amplification cycles. Amplifying the adaptor-modified nucleic acidsmay comprise 3 to 19 amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 3 to 17 amplificationcycles. Amplifying the adaptor-modified nucleic acids may comprise 4 to20 amplification cycles. Amplifying the adaptor-modified nucleic acidsmay comprise 4 to 18 amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 4 to 16 amplificationcycles. Amplifying the adaptor-modified nucleic acids may comprise 5 to20 amplification cycles. Amplifying the adaptor-modified nucleic acidsmay comprise 5 to 19 amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 5 to 18 amplificationcycles. Amplifying the adaptor-modified nucleic acids may comprise 5 to17 amplification cycles. Amplifying the adaptor-modified nucleic acidsmay comprise 5 to 16 amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 5 to 15 amplificationcycles.

Amplifying the adaptor-modified nucleic acids may comprise 20, 19, 18,17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, or 2 or feweramplification cycles. Amplifying the adaptor-modified nucleic acids maycomprise 20 or fewer amplification cycles. Amplifying theadaptor-modified nucleic acids may comprise 18 or fewer amplificationcycles. Amplifying the adaptor-modified nucleic acids may comprise 16 orfewer amplification cycles. Amplifying the adaptor-modified nucleicacids may comprise 15 or fewer amplification cycles.

The method may further comprise fragmenting the plurality of nucleicacids to produce a plurality of fragmented nucleic acids. The pluralityof nucleic acids may be fragmented prior to attaching the adaptors tothe plurality of nucleic acids. The plurality of nucleic acids may befragmented after attachment of the adaptors to the plurality of nucleicacids. The plurality of nucleic acids may be fragmented prior toamplification of the adaptor-modified nucleic acids. The plurality ofnucleic acids may be fragmented after amplification of theadaptor-modified nucleic acids. Fragmenting the plurality of nucleicacids may comprise use of one or more restriction enzymes. Fragmentingthe plurality of nucleic acids may comprise use of a sonicator.Fragmenting the plurality of nucleic acids may comprise shearing thenucleic acids.

The method may further comprise conducting an end repair reaction on theplurality of nucleic acids to produce a plurality of end repairednucleic acids. The end repair reaction may be conducted prior toattaching the adaptors to the plurality of nucleic acids. The end repairreaction may be conducted after attaching the adaptors to the pluralityof nucleic acids. The end repair reaction may be conducted prior toamplification of the adaptor-modified nucleic acids. The end repairreaction may be conducted after amplification of the adaptor-modifiednucleic acids. The end repair reaction may be conducted prior tofragmenting the plurality of nucleic acids. The end repair reaction maybe conducted after fragmenting the plurality of nucleic acids.Conducting the end repair reaction may comprise use of one or more endrepair enzymes.

The method may further comprise conducting an A-tailing reaction on theplurality of nucleic acids to produce a plurality of A-tailed nucleicacids. The A-tailing reaction may be conducted prior to attaching theadaptors to the plurality of nucleic acids. The A-tailing reaction maybe conducted after attaching the adaptors to the plurality of nucleicacids. The A-tailing reaction may be conducted prior to amplification ofthe adaptor-modified nucleic acids. The A-tailing reaction may beconducted after amplification of the adaptor-modified nucleic acids. TheA-tailing reaction may be conducted prior to fragmenting the pluralityof nucleic acids. The A-tailing reaction may be conducted afterfragmenting the plurality of nucleic acids. The A-tailing reaction maybe conducted prior to end repair of the plurality of nucleic acids. TheA-tailing reaction may be conducted after end repair of the plurality ofnucleic acids. Conducting the A-tailing reaction may comprise use of oneor more A-tailing enzymes.

The method may further comprise contacting the plurality of nucleicacids with a plurality of molecular barcodes to produce a plurality ofbarcoded nucleic acids. Producing the plurality of barcoded nucleicacids may occur prior to attaching the adaptors to the plurality ofnucleic acids. Producing the plurality of barcoded nucleic acids mayoccur after attaching the adaptors to the plurality of nucleic acids.Producing the plurality of barcoded nucleic acids may occur prior toamplification of the adaptor-modified nucleic acids. Producing theplurality of barcoded nucleic acids may occur after amplification of theadaptor-modified nucleic acids. Producing the plurality of barcodednucleic acids may occur prior to fragmenting the plurality of nucleicacids. Producing the plurality of barcoded nucleic acids may occur afterfragmenting the plurality of nucleic acids. Producing the plurality ofbarcoded nucleic acids may occur prior to end repair of the plurality ofnucleic acids. Producing the plurality of barcoded nucleic acids mayoccur after end repair the plurality of nucleic acids. Producing theplurality of barcoded nucleic acids may occur prior to A-tailing of theplurality of nucleic acids. Producing the plurality of barcoded nucleicacids may occur after A-tailing of the plurality of nucleic acids. Thebarcode may enable differentiation of two or more molecules of the samemolecular species. The barcode may enable quantification of one or moremolecules. The barcode may be a molecular barcode. The molecular barcodemay be used to differentiate two or more molecules of the same molecularspecies. The molecular barcode may be used to differentiate two or moremolecules of the same genomic region. The barcode may be a sample index.The sample index may be used to identify a sample from which themolecule (e.g., nucleic acid) originated from. For example, moleculesfrom a first sample may be associated with a first sample index, whereasmolecules from a second sample may be associated with a second sampleindex. The sample index from two or more samples may be different. Thetwo or more samples may be from the same subject. The two or moresamples may be from two or more subjects. The two or more samples may beobtained at the same time. Alternatively, or additionally, the two ormore samples may be obtained at two or more time points.

The method may further comprise contacting the plurality of nucleicacids with a plurality of sequencing adaptors to produce a plurality ofsequencer-adapted nucleic acids. Producing the plurality ofsequencer-adapted nucleic acids may occur prior to attaching theadaptors to the plurality of nucleic acids. Producing the plurality ofsequencer-adapted nucleic acids may occur after attaching the adaptorsto the plurality of nucleic acids. Producing the plurality ofsequencer-adapted nucleic acids may occur prior to amplification of theadaptor-modified nucleic acids. Producing the plurality ofsequencer-adapted nucleic acids may occur after amplification of theadaptor-modified nucleic acids. Producing the plurality ofsequencer-adapted nucleic acids may occur prior to fragmenting theplurality of nucleic acids. Producing the plurality of sequencer-adaptednucleic acids may occur after fragmenting the plurality of nucleicacids. Producing the plurality of sequencer-adapted nucleic acids mayoccur prior to end repair of the plurality of nucleic acids. Producingthe plurality of sequencer-adapted nucleic acids may occur after endrepair the plurality of nucleic acids. Producing the plurality ofsequencer-adapted nucleic acids may occur prior to A-tailing of theplurality of nucleic acids. Producing the plurality of sequencer-adaptednucleic acids may occur after A-tailing of the plurality of nucleicacids. Producing the plurality of sequencer-adapted nucleic acids mayoccur prior to producing the barcoded nucleic acids. Producing theplurality of sequencer-adapted nucleic acids may occur after producingthe barcoded nucleic acids. The sequencing adaptor may enable sequencingof the nucleic acids.

The method may further comprise contacting the plurality of nucleicacids with a plurality of primer adaptors to produce a plurality ofprimer-adapted nucleic acids. Producing the plurality of primer-adaptednucleic acids may occur prior to attaching the adaptors to the pluralityof nucleic acids. Producing the plurality of primer-adapted nucleicacids may occur after attaching the adaptors to the plurality of nucleicacids. Producing the plurality of primer-adapted nucleic acids may occurprior to amplification of the adaptor-modified nucleic acids. Producingthe plurality of primer-adapted nucleic acids may occur afteramplification of the adaptor-modified nucleic acids. Producing theplurality of primer-adapted nucleic acids may occur prior to fragmentingthe plurality of nucleic acids. Producing the plurality ofprimer-adapted nucleic acids may occur after fragmenting the pluralityof nucleic acids. Producing the plurality of primer-adapted nucleicacids may occur prior to end repair of the plurality of nucleic acids.Producing the plurality of primer-adapted nucleic acids may occur afterend repair the plurality of nucleic acids. Producing the plurality ofprimer-adapted nucleic acids may occur prior to A-tailing of theplurality of nucleic acids. Producing the plurality of primer-adaptednucleic acids may occur after A-tailing of the plurality of nucleicacids. Producing the plurality of primer-adapted nucleic acids may occurprior to producing the barcoded nucleic acids. Producing the pluralityof primer-adapted nucleic acids may occur after producing the barcodednucleic acids. Producing the plurality of primer-adapted nucleic acidsmay occur prior to producing the sequencer-adapted nucleic acids.Producing the plurality of primer-adapted nucleic acids may occur afterproducing the sequencer-adapted nucleic acids. Producing the pluralityof primer-adapted nucleic acids may comprise ligating the primeradaptors to the nucleic acids. The primer adaptor may enable sequencingof the nucleic acids. The primer adaptor may enable amplification of thenucleic acids.

The method may further comprise conducting a hybridization reaction. Thehybridization reaction may comprise use of a solid support. Thehybridization reaction may comprise hybridizing the plurality of nucleicacids to the solid support. The hybridization reaction may comprise useof a plurality of beads. The hybridization reaction may comprisehybridizing the plurality of nucleic acids to the plurality of beads.The method may further comprise conducting a hybridization reactionafter an enzymatic reaction. The enzymatic reaction may comprise aligation reaction. The enzymatic reaction may comprise a fragmentationreaction. The enzymatic reaction may comprise an end repair reaction.The enzymatic reaction may comprise an A-tailing reaction. The enzymaticreaction may comprise an amplification reaction. The method may furthercomprise conducting a hybridization reaction after one or more reactionsselected from a group consisting of a ligation reaction, fragmentationreaction, end repair reaction, A-tailing reaction, and amplificationreaction. The method may further comprise conducting a hybridizationreaction after two or more reactions selected from a group consisting ofa ligation reaction, fragmentation reaction, end repair reaction,A-tailing reaction, and amplification reaction. The method may furthercomprise conducting a hybridization reaction after three or morereactions selected from a group consisting of a ligation reaction,fragmentation reaction, end repair reaction, A-tailing reaction, andamplification reaction. The method may further comprise conducting ahybridization reaction after four or more reactions selected from agroup consisting of a ligation reaction, fragmentation reaction, endrepair reaction, A-tailing reaction, and amplification reaction. Thehybridization reaction may be conducted after each reaction selectedfrom a group consisting of ligation reaction, fragmentation reaction,end repair reaction, A-tailing reaction, and amplification reaction.

Nucleic Acid Detection Methods

Provided herein are methods for the ultrasensitive detection of aminority nucleic acid in a heterogeneous sample. The method may comprise(a) obtaining sequence information of a cell-free DNA (cfDNA) samplederived from a subject; and (b) using sequence information derived from(a) to detect cell-free minority nucleic acids in the sample, whereinthe method is capable of detecting a percentage of the cell-freeminority nucleic acids that is less than 2% of total cfDNA. The minoritynucleic acid may refer to a nucleic acid that originated from a cell ortissue that is different from a normal cell or tissue from the subject.For example, the subject may be infected with a pathogen such as abacteria and the minority nucleic acid may be a nucleic acid from thepathogen. In another example, the subject is a recipient of a cell,tissue or organ from a donor and the minority nucleic acid may be anucleic acid originating from the cell, tissue or organ from the donor.In another example, the subject is a pregnant subject and the minoritynucleic acid may be a nucleic acid originating from a fetus. The methodmay comprise using the sequence information to detect one or moresomatic mutations in the fetus. The method may comprise using thesequence information to detect one or more post-zygotic mutations in thefetus. Alternatively, the subject may be suffering from a cancer and theminority nucleic acid may be a nucleic acid originating from a cancercell.

Provided herein are methods for the ultrasensitive detection ofcirculating tumor DNA in a sample. The method may be called CAncerPersonalized Profiling by Deep Sequencing (CAPP-Seq). The method maycomprise (a) obtaining sequence information of a cell-free DNA (cfDNA)sample derived from a subject; and (b) using sequence informationderived from (a) to detect cell-free tumor DNA (ctDNA) in the sample,wherein the method is capable of detecting a percentage of ctDNA that isless than 2% of total cfDNA. CAPP-Seq may accurately quantify cell-freetumor DNA from early and advanced stage tumors. CAPP-Seq may identifymutant alleles down to 0.025% with a detection limit of <0.01%.Tumor-derived DNA levels often paralleled clinical responses to diversetherapies and CAPP-Seq may identify actionable mutations. CAPP-Seq maybe routinely applied to noninvasively detect and monitor tumors, thusfacilitating personalized cancer therapy.

Disclosed herein are methods for determining a quantity of circulatingtumor DNA (ctDNA) in a sample. The method may comprise (a) ligating oneor more adaptors to cell-free DNA (cfDNA) derived from a sample from asubject to produce one or more adaptor-ligated cfDNA; (b) performingsequencing on the one or more adaptor-ligated cfDNA, wherein theadaptor-ligated cfDNA to be sequenced is based on a selector setcomprising a plurality of genomic regions; and (c) using a computerreadable medium to determine a quantity of cfDNA originating from atumor based on the sequencing information obtained from theadaptor-ligated cfDNA. cfDNA originating from the tumor may be referredto as cell-free tumor DNA or circulating tumor DNA (ctDNA). The quantityof ctDNA may be a percentage. Determining the quantity of the ctDNA maycomprise determining the sequence of one or more genomic regions fromthe selector set. Determining the quantity of the ctDNA may comprisedetermining a number of sequence reads that contain a sequence amutation corresponding to one or more mutations in the one or moregenomic regions based on the selector set. Determining the quantity ofctDNA may comprise determining a number of sequence reads that contain asequence that does not contain a mutation corresponding to one or moremutations in the one or more genomic regions based on the selector set.Determining the quantity of ctDNA may comprise calculating a percentageof sequence reads that contain sequences with one or more mutationscorresponding to one or more mutations in the one or more genomicregions based on the selector set. For example, a selector set may beused to obtain sequencing information for a first genomic region. Thesequence information may comprise twenty sequencing reads pertaining tothe first genomic region. Analysis of the sequencing information maydetermine that two of the sequencing reads contain a mutationcorresponding to a first mutation in the first genomic region based onthe selector set and eighteen of the sequencing reads do not contain amutation corresponding to a mutation in the first genomic region basedon the selector set. Thus, the quantity of the ctDNA may be equal to thepercentage of sequencing reads with the mutation corresponding to amutation in the first genomic region, which would be 10% (e.g., 2 readsdivided by 20 reads times 100%). For sequence information pertaining totwo or more genomic regions based on the selector set, determining thequantity of ctDNA may comprise calculating an average of the percentagesthe two or more genomic regions. For example, the percentage ofsequencing reads containing a mutation corresponding to a first mutationin a first genomic region is 20% and the percentage of sequencing readscontaining a mutation corresponding to a second mutation in a secondgenomic region is 40%; the quantity of ctDNA is the average of thepercentages of the two genomic regions, which is 30% (e.g., (20%+40%)divided by 2). The quantity of ctDNA may be converted into a mass perunit volume value by multiplying the percentage of the ctDNA by theabsolute concentration of the total cell-free DNA per unit volume. Forexample, the percentage of ctDNA may be 30% and the concentration of thecell free DNA may be 10 nanograms per milliliter (ng/mL); the quantityof ctDNA may be 3 ng/mL (e.g., 0.30 times 10 ng/mL).

Alternatively, or additionally, determining the quantity of ctDNA maycomprise use of adaptors comprising a barcode sequence. Two or moreadaptors may contain two or more different barcode sequences. Thebarcode sequence may be a random sequence. A genomic region may beattached to an adaptor containing a barcode sequence. Identical genomicregions may be attached to adaptors containing different barcodesequences. Non-identical genomic regions may be attached to adaptorscontaining different barcode sequences. The barcode sequences may beused to count a number of occurrences of a genomic region. The quantityof the ctDNA may be based on counting a number of occurrences of genomicregions based on the selector set. Rather than basing the quantity ofthe ctDNA on the number of sequencing reads, the quantity of the ctDNAmay be based on the number of different barcodes associated with one ormore genomic regions. For example, ten different barcodes may beassociated with sequences containing a mutation corresponding to amutation in a first genomic region based on the selector set, resultingin a quantity of ctDNA of ten. For two or more genomic regions, thequantity of the ctDNA may be a sum of the quantity of the two or moregenomic regions. For example, ten different barcodes may be associatedwith sequences containing a mutation corresponding to a mutation in afirst genomic region and twenty different barcodes may be associatedwith sequences containing a mutation correspond to a mutation in asecond genomic region, resulting in a quantity of ctDNA of 30. Thequantity of the ctDNA may be a percentage of the total cell-free DNA.For example, ten different barcodes may be associated with sequencescontaining a mutation corresponding to a mutation in a first genomicregion and forty different barcodes may be associated with sequencesthat do not contain a mutation corresponding to a mutation in the firstgenomic region, resulting in a quantity of ctDNA of 20% (e.g., (10divided by 50) times 100%).

Disclosed herein are methods of enriching for circulating tumor DNA froma sample. The method may comprise contacting cell-free nucleic acidsfrom a sample with a plurality of oligonucleotides, wherein theplurality of oligonucleotides selectively hybridize to a plurality ofgenomic regions comprising a plurality of mutations present in >60% of apopulation of subjects suffering from a cancer.

Alternatively, the method may comprise contacting cell-free nucleicacids from a sample with a set of oligonucleotides, wherein the set ofoligonucleotides selectively hybridize to a plurality of genomicregions, wherein (a) >80% of tumors from a population of cancer subjectsinclude one or more mutations in the genomic regions; (b) the pluralityof genomic regions represent less than 1.5 Mb of the genome; and (c) theset of oligonucleotides comprise 5 or more different oligonucleotidesthat selectively hybridize to the plurality of genomic regions. Thecell-free nucleic acids may be DNA. The cell-free nucleic acids may beRNA.

Applications

The selector sets created according to the methods described herein maybe useful in the analysis of genetic alterations, particularly incomparing tumor and genomic sequences in a patient with cancer. As shownin FIG. 2, a tissue biopsy sample from the patient may be used todiscover mutations in the tumor by sequencing the genomic regions of theselector library in tumor and genomic nucleic acid samples and comparingthe results. The selector sets may be designed to identify mutations intumors from a large percentage of all patients, thus, it may not benecessary to optimize the library for each patient.

In some methods of the invention, the analysis of cfDNA for somaticmutations is compared to personalized tumor markers in an initialdataset developed from somatic mutations in a known tumor sample from anindividual. To develop such a dataset, a sample of tumor cells or knowntumor DNA may be obtained, which is compared to a germline sample.Preferably although not necessarily, a germline sample may be from theindividual.

To “analyze” may include determining a set of values associated with asample by determining a DNA sequence, and comparing the sequence againstthe sequence of a sample or set of samples from the same subject, from acontrol, from reference values, etc. as known in the art. To “analyze”can include performing a statistical analysis.

CAPP-seq may utilize hybrid selection of cfDNA corresponding to regionsof recurrent mutation for diagnosis and monitoring of cancer in anindividual patient. In such embodiments the selector set probes are usedto enrich, e.g. by hybrid selection, for ctDNA that corresponds to theregions of the genome that are most likely to contain tumor-specificsomatic mutations. The “selected” ctDNA is then amplified and sequencedto determine which of the selected genomic regions are mutated in theindividual tumor. An initial comparison is optionally made with theindividual's germline DNA sequence and/or a tumor biopsy sample from theindividual. These somatic mutations provide a means of distinguishingctDNA from germline DNA, and thus provide useful information about thepresence and quantity of tumor cells in the individual. A flow chart forthis process is provided in FIG. 22.

In other embodiments, CAPP-seq is used for cancer screening andbiopsy-free tumor genotyping, where a patient ctDNA sample is analyzedwithout reference to a biopsy sample. In some such embodiments, whereCAPP-Seq identifies a mutation in a clinically actionable target from actDNA sample, the methods include providing a therapy appropriate forthe target. Such mutations include, without limitation, rearrangementsand other mutations involving oncogenes, receptor tyrosine kinases, etc.

Further disclosed herein is a method of detecting, diagnosing,prognosing, or therapy selection for a cancer subject comprising: (a)obtaining sequence information of a cell-free DNA (cfDNA) sample derivedfrom the subject; and (b) using sequence information derived from (a) todetect cell-free non-germline DNA (cfNG-DNA) in the sample, wherein themethod is capable of detecting a percentage of cfNG-DNA that is lessthan 2% of total cfDNA. The method may be capable of detecting apercentage of ctDNA that is less than 1.5% of the total cfDNA. Themethod may be capable of detecting a percentage of cfNG-DNA that is lessthan 1% of the total cfDNA. The method may be capable of detecting apercentage of cfNG-DNA that is less than 0.5% of the total cfDNA. Themethod may be capable of detecting a percentage of cfNG-DNA that is lessthan 0.1% of the total cfDNA. The method may be capable of detecting apercentage of cfNG-DNA that is less than 0.01% of the total cfDNA. Themethod may be capable of detecting a percentage of cfNG-DNA that is lessthan 0.001% of the total cfDNA. The method may be capable of detecting apercentage of cfNG-DNA that is less than 0.0001% of the total cfDNA. Thesample may be a plasma or serum sample. The sample may be a cerebralspinal fluid sample. In some instances, the sample is not a pap smearfluid sample. In some instances, the sample is a cyst fluid sample. Insome instances, the sample is a pancreatic fluid sample. The sequenceinformation may comprise information related to at least 10, 20, 30, 40,100, 200, 300 genomic regions. The genomic regions may comprise genes,exonic regions, intronic regions, untranslated regions, non-codingregions or a combination thereof. The genomic regions may comprise twoor more of exonic regions, intronic regions, and untranslated regions.The genomic regions may comprise at least one exonic region and at leastone intronic region. At least 5% of the genomic regions may compriseintronic regions. At least about 20% of the genomic regions may compriseexonic regions. The genomic regions may comprise less than 1.5 megabases(Mb) of the genome. The genomic regions may comprise less than 1 Mb ofthe genome. The genomic regions may comprise less than 500 kilobases(kb) of the genome. The genomic regions may comprise less than 350 kb ofthe genome. The genomic regions may comprise between 100 kb to 300 kb ofthe genome. The sequence information may comprise information pertainingto 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from aselector set comprising a plurality of genomic regions. The sequenceinformation may comprise information pertaining to 25, 30, 40, 50, 60,70, 80, 90, 100 or more genomic regions from a selector set comprising aplurality of genomic regions. The sequence information may compriseinformation pertaining to a plurality of genomic regions. The pluralityof genomic regions may be based on a selector set comprising genomicregions comprising one or more mutations present in one or more subjectsfrom a population of cancer subjects. At least about 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or95% of the plurality of genomic regions may be based on a selector setcomprising genomic regions comprising one or more mutations present inone or more subjects from a population of cancer subjects. The totalsize of the genomic regions of the selector set may comprise less than1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb,200 kb, or 150 kb of the genome. The total size of the genomic regionsof the selector set may be between 100 kb to 300 kb of the genome. Theselector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table2. In some instances, the subject is not suffering from a pancreaticcancer. Obtaining sequence information may comprise performing massivelyparallel sequencing. Massively parallel sequencing may be performed on asubset of a genome of cfDNA from the cfDNA sample. The subset of thegenome may comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases(kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome. Thesubset of the genome may comprise between 100 kb to 300 kb of thegenome. Obtaining sequence information may comprise using singlemolecule barcoding. Using single molecule barcoding may compriseattaching barcodes comprising different sequences to nucleic acids fromthe cfDNA sample. The sequence information may comprise sequenceinformation pertaining to the barcodes. The method may compriseobtaining sequencing information of cell-free DNA samples from two ormore samples from the subject. The two or more samples may be the sametype of sample. The two or more samples may be two different types ofsample. The two or more samples may be obtained from the subject at thesame time point. The two or more samples may be obtained from thesubject at two or more time points. The method may comprise obtainingsequencing information of cell-free DNA samples from two or moredifferent subjects. The samples from two or more different subjects maybe indexed and pooled together prior to obtaining the sequencinginformation. Using the sequence information may comprise detecting oneor more SNVs, indels, fusions, breakpoints, structural variants,variable number of tandem repeats, hypervariable regions,minisatellites, dinucleotide repeats, trinucleotide repeats,tetranucleotide repeats, simple sequence repeats, or a combinationthereof in selected regions of the subject's genome. Using the sequenceinformation may comprise detecting one or more of SNVs, indels, copynumber variants, and rearrangements in selected regions of the subject'sgenome. Using the sequence information may comprise detecting two ormore of SNVs, indels, copy number variants, and rearrangements inselected regions of the subject's genome. Using the sequence informationmay comprise detecting at least one SNV, indel, copy number variant, andrearrangement in selected regions of the subject's genome. In someinstances, detecting does not involve performing digital PCR (dPCR).Detecting cell-free non-germline DNA may comprise applying an algorithmto the sequence information to determine a quantity of one or moregenomic regions from a selector set. The selector set may comprise aplurality of genomic regions comprising one or more mutations present inone or more cancer subjects from a population of cancer subjects. Theselector set may comprise a plurality of genomic regions comprising oneor more mutations present in at least about 60% of cancer subjects frompopulation of cancer subjects. The cfNG-DNA may be derived from a tumorin the subject. The method may further comprise detecting a cancer inthe subject based on the detection of the cfNG-DNA. The method mayfurther comprise diagnosing a cancer in the subject based on thedetection of the cfNG-DNA. Diagnosing the cancer may have a sensitivityof at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. Diagnosing the cancer may have a specificityof at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. The method may further comprise prognosing acancer in the subject based on the detection of the cfNG-DNA. Prognosingthe cancer may have a sensitivity of at least about 75%, 77%, 80%, 82%,85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.Prognosing the cancer may have a specificity of at least about 75%, 77%,80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.The method may further comprise determining a therapeutic regimen forthe subject based on the detection of the cfNG-DNA. The method mayfurther comprise administering an anti-cancer therapy to the subjectbased on the detection of the cfNG-DNA. The cfNG-DNA may be derived froma fetus in the subject. The method may further comprise diagnosing adisease or condition in the fetus based on the detection of thecfNG-DNA. Diagnosing the disease or condition in the fetus may have asensitivity of at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the disease orcondition in the fetus may have a specificity of at least about 75%,77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or99%. The cfNG-DNA may be derived from a transplanted organ, cell ortissue in the subject. The method may further comprise diagnosing anorgan transplant rejection in the subject based on the detection of thecfNG-DNA. Diagnosing the organ transplant rejection may have asensitivity of at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the organtransplant rejection may have a specificity of at least about 75%, 77%,80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.The method may further comprise prognosing a risk of organ transplantrejection in the subject based on the detection of the cfNG-DNA.Prognosing the risk of organ transplant rejection may have a sensitivityof at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, or 99%. Prognosing the risk of organ transplantrejection may have a specificity of at least about 75%, 77%, 80%, 82%,85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Themethod may further comprise determining an immunosuppresive therapy forthe subject based on the detection of the cfNG-DNA. The method mayfurther comprise administering an immunosuppresive therapy to thesubject based on the detection of the cfNG-DNA.

Further disclosed herein are methods of detecting, diagnosing, orprognosing a status or outcome of a cancer in a subject. The method maycomprise (a) obtaining sequence information of a cell-free DNA (cfDNA)sample derived from the subject; (b) using sequence information derivedfrom (a) to detect cell-free tumor DNA (ctDNA) in the sample wherein themethod is capable of detecting a percentage of ctDNA that is less than2% of total cfDNA. The method may be capable of detecting a percentageof ctDNA that is less than 1.5% of the total cfDNA. The method may becapable of detecting a percentage of ctDNA that is less than 1% of thetotal cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 0.5% of the total cfDNA. The method may becapable of detecting a percentage of ctDNA that is less than 0.1% of thetotal cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 0.01% of the total cfDNA. The method may becapable of detecting a percentage of ctDNA that is less than 0.001% ofthe total cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 0.0001% of the total cfDNA. The sample may be aplasma or serum sample. The sample may be a cerebral spinal fluidsample. In some instances, the sample is not a pap smear fluid sample.In some instances, the sample is a cyst fluid sample. In some instances,the sample is a pancreatic fluid sample. The sequence information maycomprise information related to at least 10, 20, 30, 40, 100, 200, 300genomic regions. The genomic regions may comprise genes, exonic regions,intronic regions, untranslated regions, non-coding regions or acombination thereof. The genomic regions may comprise two or more ofexonic regions, intronic regions, and untranslated regions. The genomicregions may comprise at least one exonic region and at least oneintronic region. At least 5% of the genomic regions may compriseintronic regions. At least about 20% of the genomic regions may compriseexonic regions. The genomic regions may comprise less than 1.5 megabases(Mb) of the genome. The genomic regions may comprise less than 1 Mb ofthe genome. The genomic regions may comprise less than 500 kilobases(kb) of the genome. The genomic regions may comprise less than 350 kb ofthe genome. The genomic regions may comprise between 100 kb to 300 kb ofthe genome. The sequence information may comprise information pertainingto 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from aselector set comprising a plurality of genomic regions. The sequenceinformation may comprise information pertaining to 25, 30, 40, 50, 60,70, 80, 90, 100 or more genomic regions from a selector set comprising aplurality of genomic regions. The sequence information may compriseinformation pertaining to a plurality of genomic regions. The pluralityof genomic regions may be based on a selector set comprising genomicregions comprising one or more mutations present in one or more subjectsfrom a population of cancer subjects. At least about 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or95% of the plurality of genomic regions may be based on a selector setcomprising genomic regions comprising one or more mutations present inone or more subjects from a population of cancer subjects. The totalsize of the genomic regions of the selector set may comprise less than1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb,200 kb, or 150 kb of the genome. The total size of the genomic regionsof the selector set may be between 100 kb to 300 kb of the genome. Theselector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table2. In some instances, the subject is not suffering from a pancreaticcancer. Obtaining sequence information may comprise performing massivelyparallel sequencing. Massively parallel sequencing may be performed on asubset of a genome of cfDNA from the cfDNA sample. The subset of thegenome may comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases(kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome. Thesubset of the genome may comprise between 100 kb to 300 kb of thegenome. Obtaining sequence information may comprise using singlemolecule barcoding. Using single molecule barcoding may compriseattaching barcodes comprising different sequences to nucleic acids fromthe cfDNA sample. The sequence information may comprise sequenceinformation pertaining to the barcodes. The method may compriseobtaining sequencing information of cell-free DNA samples from two ormore samples from the subject. The two or more samples may be the sametype of sample. The two or more samples may be two different types ofsample. The two or more samples may be obtained from the subject at thesame time point. The two or more samples may be obtained from thesubject at two or more time points. The method may comprise obtainingsequencing information of cell-free DNA samples from two or moredifferent subjects. The samples from two or more different subjects maybe indexed and pooled together prior to obtaining the sequencinginformation. Using the sequence information may comprise detecting oneor more SNVs, indels, fusions, breakpoints, structural variants,variable number of tandem repeats, hypervariable regions,minisatellites, dinucleotide repeats, trinucleotide repeats,tetranucleotide repeats, simple sequence repeats, or a combinationthereof in selected regions of the subject's genome. Using the sequenceinformation may comprise detecting one or more of SNVs, indels, copynumber variants, and rearrangements in selected regions of the subject'sgenome. Using the sequence information may comprise detecting two ormore of SNVs, indels, copy number variants, and rearrangements inselected regions of the subject's genome. Using the sequence informationmay comprise detecting at least one SNV, indel, copy number variant, andrearrangement in selected regions of the subject's genome. In someinstances, detecting does not involve performing digital PCR (dPCR).Detecting ctDNA may comprise applying an algorithm to the sequenceinformation to determine a quantity of one or more genomic regions froma selector set. The selector set may comprise a plurality of genomicregions comprising one or more mutations present in one or more cancersubjects from a population of cancer subjects. The selector set maycomprise a plurality of genomic regions comprising one or more mutationspresent in at least about 60% of cancer subjects from population ofcancer subjects. The ctDNA may be derived from a tumor in the subject.The method may further comprise detecting a cancer in the subject basedon the detection of the ctDNA. The method may further comprisediagnosing a cancer in the subject based on the detection of the ctDNA.Diagnosing the cancer may have a sensitivity of at least about 75%, 77%,80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.Diagnosing the cancer may have a specificity of at least about 75%, 77%,80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.The method may further comprise prognosing a cancer in the subject basedon the detection of the ctDNA. Prognosing the cancer may have asensitivity of at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Prognosing the cancer mayhave a specificity of at least about 75%, 77%, 80%, 82%, 85%, 87%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. The method may furthercomprise determining a therapeutic regimen for the subject based on thedetection of the ctDNA. The method may further comprise administering ananti-cancer therapy to the subject based on the detection of the ctDNA.

Further disclosed herein are methods of diagnosing a status or outcomeof a cancer in a subject. The method may comprise (a) obtaining sequenceinformation of cell-free genomic DNA derived from a sample from asubject, wherein the sequence information is derived from genomicregions that are mutated in at least 80% of a population of subjectsafflicted with a cancer; and (b) diagnosing a cancer selected from agroup consisting of lung cancer, breast cancer, colorectal cancer andprostate cancer in the subject based on the sequence information,wherein the method has a sensitivity of 80%. The regions that aremutated may comprise a total size of less than 1.5 Mb of the genome. Theregions that are mutated may comprise a total size of less than 1 Mb ofthe genome. The regions that are mutated may comprise a total size ofless than 500 kb of the genome. The regions that are mutated maycomprise a total size of less than 350 kb of the genome. The regionsthat are mutated may comprise a total size between 100 kb-300 kb of thegenome. The sequence information may be derived from 2 or more regions.The sequence may be derived from 10 or more regions. The sequence may bederived from 50 or more regions. The population of subjects afflictedwith the cancer may be subjects from one or more databases. The one ormore databases may comprise The Cancer Genome Atlas (TCGA). The sequenceinformation may comprise information pertaining to at least one mutationthat may be present in at least about 60% of the population of subjectsafflicted with the cancer. The sequence information may compriseinformation pertaining to at least one mutation that may be present inat least about 70% of the population of subjects afflicted with thecancer. The sequence information may comprise information pertaining toat least one mutation that may be present in at least about 80% of thepopulation of subjects afflicted with the cancer. The sequenceinformation may comprise information pertaining to at least one mutationthat may be present in at least about 90% of the population of subjectsafflicted with the cancer. The sequence information may compriseinformation pertaining to at least one mutation that may be present inat least about 95% of the population of subjects afflicted with thecancer. The sequence information may comprise information pertaining toat least one mutation that may be present in at least about 99% of thepopulation of subjects afflicted with the cancer. The sequenceinformation may be derived from regions that are mutated in at least 85%of the population of subjects afflicted with the cancer. The sequenceinformation may be derived from regions that are mutated in at least 90%of the population of subjects afflicted with the cancer. The sequenceinformation may be derived from regions that are mutated in at least 95%of the population of subjects afflicted with the cancer. The sequenceinformation may be derived from regions that are mutated in at least 99%of the population of subjects afflicted with the cancer. The obtainingsequence information may comprise sequencing noncoding regions. Thenoncoding regions may comprise one or more 1ncRNA, snoRNA, siRNA, miRNA,piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA,x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs,aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof. Obtainingsequence information may comprise sequencing protein coding regions. Theprotein coding regions may comprise one or more exons, introns,untranslated regions, or a combination thereof. In some instances, atleast one of the regions does not comprise KRAS or EGFR. In someinstances, at least two of the regions do not comprise KRAS and EGFR. Insome instances, at least one of the regions does not comprise KRAS,EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least twoof the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, orBRCA1. In some instances, at least three of the regions do not compriseKRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, atleast four of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF,EZH2, or BRCA1. The method may further comprise detecting mutations inthe regions based on the sequencing information. Diagnosing the cancermay be based on the detection of the mutations. The detection of atleast 3 mutations may be indicative of the cancer. The detection of oneor more mutations in three or more regions may be indicative of thecancer. The breast cancer may be a BRCA1 cancer. The method may have asensitivity of at least 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%. The method may have a specificity of at least 70%,72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99%. The method may further comprise providing acomputer-generated report comprising the diagnosis of the cancer.

Further disclosed herein are methods of prognosing a status or outcomeof a cancer in a subject. The method may comprise (a) obtaining sequenceinformation of cell-free genomic DNA derived from a sample from asubject, wherein the sequence information is derived from regions thatare mutated in at least 80% of a population of subjects afflicted with acondition; and (b) determining a prognosis of a condition in the subjectbased on the sequence information. The regions that are mutated maycomprise a total size of less than 1.5 Mb of the genome. The regionsthat are mutated may comprise a total size of less than 1 Mb of thegenome. The regions that are mutated may comprise a total size of lessthan 500 kb of the genome. The regions that are mutated may comprise atotal size of less than 350 kb of the genome. The regions that aremutated may comprise a total size between 100 kb-300 kb of the genome.The sequence information may be derived from 2 or more regions. Thesequence may be derived from 10 or more regions. The sequence may bederived from 50 or more regions. The population of subjects afflictedwith the condition may be subjects from one or more databases. The oneor more databases may comprise The Cancer Genome Atlas (TCGA). Thesequence information may comprise information pertaining to at least onemutation that may be present in at least about 60% of the population ofsubjects afflicted with the condition. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 70% of the population of subjects afflictedwith the condition. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 80% of the population of subjects afflicted with the condition.The sequence information may comprise information pertaining to at leastone mutation that may be present in at least about 90% of the populationof subjects afflicted with the condition. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 95% of the population of subjects afflictedwith the condition. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 99% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 85% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 90% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 95% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 99% of the population of subjects afflicted with the condition.Obtaining sequence information may comprise sequencing noncodingregions. The noncoding regions may comprise one or more 1ncRNA, snoRNA,siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA,uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes,GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.Obtaining sequence information may comprise sequencing protein codingregions. The protein coding regions may comprise one or more exons,introns, untranslated regions, or a combination thereof. In someinstances, at least one of the regions does not comprise KRAS or EGFR.In some instances, at least two of the regions do not comprise KRAS andEGFR. In some instances, at least one of the regions does not compriseKRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, atleast two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF,EZH2, or BRCA1. In some instances, at least three of the regions do notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least four of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. The method may further comprise detectingmutations in the regions based on the sequencing information. Prognosingthe condition may be based on the detection of the mutations. Thedetection of at least 3 mutations may be indicative of an outcome of thecondition. The detection of one or more mutations in three or moreregions may be indicative of an outcome of the condition. The conditionmay be a cancer. The cancer may be a solid tumor. The solid tumor may benon-small cell lung cancer (NSCLC). The cancer may be a breast cancer.The breast cancer may be a BRCA1 cancer. The cancer may be a lungcancer, colorectal cancer, prostate cancer, ovarian cancer, esophagealcancer, breast cancer, lymphoma, or leukemia. The method may have asensitivity of at least 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%,93%, 94%, 95%, 96%, 97%, 98%, or 99%. The method may have a specificityof at least 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%,94%, 95%, 96%, 97%, 98%, or 99%. The method may further compriseproviding a computer-generated report comprising the prognosis of thecondition.

Disclosed herein are methods for detecting at least 50% of stage Icancer with a specificity of greater than 90%. The method may comprise(a) performing sequencing on cell-free DNA derived from a sample,wherein the cell-free DNA to be sequenced is based on a selector setcomprising a plurality of genomic regions; (b) using a computer readablemedium to determine a quantity of the cell-free DNA based on thesequencing information of the cell-free DNA; and (c) detecting a stage Icancer in the sample based on the quantity of the cell-free DNA.Determining the quantity of the cell-free DNA may comprise determiningabsolute quantities of the cell-free DNA. The quantity of the cell-freeDNA may be determined by counting sequencing reads pertaining to thecell-free DNA. The quantity of the cell-free DNA may be determined byquantitative PCR. The quantity of the cell-free DNA may be determined bymolecular barcoding of the cell-free DNA (cfDNA). Molecular barcoding ofthe cfDNA may comprise attaching barcodes to one or more ends of thecfDNA. The barcode may comprise a random sequence. Two or more barcodesmay comprise two or more different random sequences. The barcode maycomprise an adaptor sequence. Two or more barcodes may comprise the sameadaptor sequence. The barcode may comprise a primer sequence. Two ormore barcodes may comprise the same primer sequence. The primer sequencemay be a PCR primer sequence. The primer sequence may be a sequencingprimer. Attaching the barcodes to one or more ends of the ctDNA maycomprise ligating the barcodes to the one or more ends of the ctDNA.Sequencing may comprise massively parallel sequencing. The selector setmay comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45,50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regionsfrom Table 2. At least 20%, 30%, 35%, 40%, 455, 50%, 55%, 60%, 65%, 70%,75%, 80%, 85%, 90%, or 95% or more of the genomic regions in theselector set are based on genomic regions from Table 2. The plurality ofgenomic regions may comprise one or more mutations present in at least60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%,95%, 97% or 99% or more of a population of subjects suffering from thecancer. The total size of the plurality of genomic regions of theselector set may comprise less than 1.5 megabases (Mb), 1 Mb, 500kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome.The total size of the plurality of genomic regions of the selector setmay be between 100 kb to 300 kb of a genome. The method may have asensitivity of at least 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%,97%, or 99% or more. The method may detect at least 52%, 55%, 57%, 60%,62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% ormore of stage I cancer.

Disclosed herein are methods for detecting at least 60% of stage IIcancer with a specificity of greater than 90% comprising (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced is based on a selector set comprising a plurality ofgenomic regions; (b) using a computer readable medium to determine aquantity of the cell-free DNA based on the sequencing information of thecell-free DNA; and (c) detecting a stage II cancer in the sample basedon the quantity of the cell-free DNA. Determining the quantity of thecell-free DNA may comprise determining absolute quantities of thecell-free DNA. The quantity of the cell-free DNA may be determined bycounting sequencing reads pertaining to the cell-free DNA. The quantityof the cell-free DNA may be determined by quantitative PCR. The quantityof the cell-free DNA may be determined by molecular barcoding of thecell-free DNA (cfDNA). Molecular barcoding of the cfDNA may compriseattaching barcodes to one or more ends of the cfDNA. The barcode maycomprise a random sequence. Two or more barcodes may comprise two ormore different random sequences. The barcode may comprise an adaptorsequence. Two or more barcodes may comprise the same adaptor sequence.The barcode may comprise a primer sequence. Two or more barcodes maycomprise the same primer sequence. The primer sequence may be a PCRprimer sequence. The primer sequence may be a sequencing primer.Attaching the barcodes to one or more ends of the ctDNA may compriseligating the barcodes to the one or more ends of the ctDNA. Sequencingmay comprise massively parallel sequencing. The selector set maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions fromTable 2. At least 20%, 30%, 35%, 40%, 455, 50%, 55%, 60%, 65%, 70%, 75%,80%, 85%, 90%, or 95% or more of the genomic regions in the selector setmay be based on genomic regions from Table 2. The plurality of genomicregions may comprise one or more mutations present in at least 60%, 62%,65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or99% or more of a population of subjects suffering from the cancer. Thetotal size of the plurality of genomic regions of the selector set maycomprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb,300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 300 kb of a genome. The method may have a sensitivity of at least75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. Themethod may detect at least 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or more of stage II cancer.

Disclosed herein are methods for detecting at least 60% of stage IIIcancer with a specificity of greater than 90% comprising (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced is based on a selector set comprising a plurality ofgenomic regions; (b) using a computer readable medium to determine aquantity of the cell-free DNA based on the sequencing information of thecell-free DNA; and (c) detecting a stage III cancer in the sample basedon the quantity of the cell-free DNA. Determining the quantity of thecell-free DNA may comprise determining absolute quantities of thecell-free DNA. The quantity of the cell-free DNA may be determined bycounting sequencing reads pertaining to the cell-free DNA. The quantityof the cell-free DNA may be determined by quantitative PCR. The quantityof the cell-free DNA may be determined by molecular barcoding of thecell-free DNA (cfDNA). Molecular barcoding of the cfDNA may compriseattaching barcodes to one or more ends of the cfDNA. The barcode maycomprise a random sequence. Two or more barcodes may comprise two ormore different random sequences. The barcode may comprise an adaptorsequence. Two or more barcodes may comprise the same adaptor sequence.The barcode may comprise a primer sequence. Two or more barcodes maycomprise the same primer sequence. The primer sequence may be a PCRprimer sequence. The primer sequence may be a sequencing primer.Attaching the barcodes to one or more ends of the ctDNA may compriseligating the barcodes to the one or more ends of the ctDNA. Sequencingmay comprise massively parallel sequencing. The selector set maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions fromTable 2. At least 20%, 30%, 35%, 40%, 455, 50%, 55%, 60%, 65%, 70%, 75%,80%, 85%, 90%, or 95% or more of the genomic regions in the selector setmay be based on genomic regions from Table 2. The plurality of genomicregions may comprise one or more mutations present in at least 60%, 62%,65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or99% or more of a population of subjects suffering from the cancer. Thetotal size of the plurality of genomic regions of the selector set maycomprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb,300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 300 kb of a genome. The method may have a sensitivity of at least75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. Themethod may detect at least 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or more of stage III cancer.

Disclosed herein are methods for detecting at least 60% of stage IVcancer with a specificity of greater than 90% comprising (a) performingsequencing on cell-free DNA derived from a sample, wherein the cell-freeDNA to be sequenced is based on a selector set comprising a plurality ofgenomic regions; (b) using a computer readable medium to determine aquantity of the cell-free DNA based on the sequencing information of thecell-free DNA; and (c) detecting a stage IV cancer in the sample basedon the quantity of the cell-free DNA. Determining the quantity of thecell-free DNA may comprise determining absolute quantities of thecell-free DNA. The quantity of the cell-free DNA may be determined bycounting sequencing reads pertaining to the cell-free DNA. The quantityof the cell-free DNA may be determined by quantitative PCR. The quantityof the cell-free DNA may be determined by molecular barcoding of thecell-free DNA (cfDNA). Molecular barcoding of the cfDNA may compriseattaching barcodes to one or more ends of the cfDNA. The barcode maycomprise a random sequence. Two or more barcodes may comprise two ormore different random sequences. The barcode may comprise an adaptorsequence. Two or more barcodes may comprise the same adaptor sequence.The barcode may comprise a primer sequence. Two or more barcodes maycomprise the same primer sequence. The primer sequence may be a PCRprimer sequence. The primer sequence may be a sequencing primer.Attaching the barcodes to one or more ends of the ctDNA may compriseligating the barcodes to the one or more ends of the ctDNA. Sequencingmay comprise massively parallel sequencing. The selector set maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions fromTable 2. At least 20%, 30%, 35%, 40%, 455, 50%, 55%, 60%, 65%, 70%, 75%,80%, 85%, 90%, or 95% or more of the genomic regions in the selector setmay be based on genomic regions from Table 2. The plurality of genomicregions may comprise one or more mutations present in at least 60%, 62%,65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or99% or more of a population of subjects suffering from the cancer. Thetotal size of the plurality of genomic regions of the selector set maycomprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb,300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of theplurality of genomic regions of the selector set may be between 100 kbto 300 kb of a genome. The method may have a sensitivity of at least75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. Themethod may detect at least 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%,85%, 87%, 90%, 92%, 95%, 97% or more of stage IV cancer.

Further disclosed herein are methods of selecting a therapy for asubject suffering from a cancer. The method may comprise (a) obtainingsequence information of a cell-free DNA (cfDNA) sample derived from thesubject; (b) using sequence information derived from (a) to detectcell-free tumor DNA (ctDNA) in the sample; and (c) determining a therapyfor the subject based on the detection of the ctDNA, wherein the methodis capable of detecting a percentage of ctDNA that is less than 2% oftotal cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 1.5% of the total cfDNA. The method may becapable of detecting a percentage of ctDNA that is less than 1% of thetotal cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 0.5% of the total cfDNA. The method may becapable of detecting a percentage of ctDNA that is less than 0.1% of thetotal cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 0.01% of the total cfDNA. The method may becapable of detecting a percentage of ctDNA that is less than 0.001% ofthe total cfDNA. The method may be capable of detecting a percentage ofctDNA that is less than 0.0001% of the total cfDNA. The sample may be aplasma or serum sample. The sample may be a cerebral spinal fluidsample. In some instances, the sample is not a pap smear fluid sample.In some instances, the sample is a cyst fluid sample. In some instances,the sample is a pancreatic fluid sample. The sequence information maycomprise information related to at least 10, 20, 30, 40, 100, 200, 300genomic regions. The genomic regions may comprise genes, exonic regions,intronic regions, untranslated regions, non-coding regions or acombination thereof. The genomic regions may comprise two or more ofexonic regions, intronic regions, and untranslated regions. The genomicregions may comprise at least one exonic region and at least oneintronic region. At least 5% of the genomic regions may compriseintronic regions. At least about 20% of the genomic regions may compriseexonic regions. The genomic regions may comprise less than 1.5 megabases(Mb) of the genome. The genomic regions may comprise less than 1 Mb ofthe genome. The genomic regions may comprise less than 500 kilobases(kb) of the genome. The genomic regions may comprise less than 350 kb ofthe genome. The genomic regions may comprise between 100 kb to 300 kb ofthe genome. The sequence information may comprise information pertainingto 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from aselector set comprising a plurality of genomic regions. The sequenceinformation may comprise information pertaining to 25, 30, 40, 50, 60,70, 80, 90, 100 or more genomic regions from a selector set comprising aplurality of genomic regions. The sequence information may compriseinformation pertaining to a plurality of genomic regions. The pluralityof genomic regions may be based on a selector set comprising genomicregions comprising one or more mutations present in one or more subjectsfrom a population of cancer subjects. At least about 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or95% of the plurality of genomic regions may be based on a selector setcomprising genomic regions comprising one or more mutations present inone or more subjects from a population of cancer subjects. The totalsize of the genomic regions of the selector set may comprise less than1.5 megabases (Mb), 1 Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb,200 kb, or 150 kb of the genome. The total size of the genomic regionsof the selector set may be between 100 kb to 300 kb of the genome. Theselector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table2. In some instances, the subject is not suffering from a pancreaticcancer. Obtaining sequence information may comprise performing massivelyparallel sequencing. Massively parallel sequencing may be performed on asubset of a genome of cfDNA from the cfDNA sample. The subset of thegenome may comprise less than 1.5 megabases (Mb), 1 Mb, 500 kilobases(kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome. Thesubset of the genome may comprise between 100 kb to 300 kb of thegenome. Obtaining sequence information may comprise using singlemolecule barcoding. Using single molecule barcoding may compriseattaching barcodes comprising different sequences to nucleic acids fromthe cfDNA sample. The sequence information may comprise sequenceinformation pertaining to the barcodes. The method may compriseobtaining sequencing information of cell-free DNA samples from two ormore samples from the subject. The two or more samples may be the sametype of sample. The two or more samples may be two different types ofsample. The two or more samples may be obtained from the subject at thesame time point. The two or more samples may be obtained from thesubject at two or more time points. The method may comprise obtainingsequencing information of cell-free DNA samples from two or moredifferent subjects. The samples from two or more different subjects maybe indexed and pooled together prior to obtaining the sequencinginformation. Using the sequence information may comprise detecting oneor more SNVs, indels, fusions, breakpoints, structural variants,variable number of tandem repeats, hypervariable regions,minisatellites, dinucleotide repeats, trinucleotide repeats,tetranucleotide repeats, simple sequence repeats, or a combinationthereof in selected regions of the subject's genome. Using the sequenceinformation may comprise detecting one or more of SNVs, indels, copynumber variants, and rearrangements in selected regions of the subject'sgenome. Using the sequence information may comprise detecting two ormore of SNVs, indels, copy number variants, and rearrangements inselected regions of the subject's genome. Using the sequence informationmay comprise detecting at least one SNV, indel, copy number variant, andrearrangement in selected regions of the subject's genome. In someinstances, detecting does not involve performing digital PCR (dPCR).Detecting ctDNA may comprise applying an algorithm to the sequenceinformation to determine a quantity of one or more genomic regions froma selector set. The selector set may comprise a plurality of genomicregions comprising one or more mutations present in one or more cancersubjects from a population of cancer subjects. The selector set maycomprise a plurality of genomic regions comprising one or more mutationspresent in at least about 60% of cancer subjects from population ofcancer subjects. The ctDNA may be derived from a tumor in the subject.Determining the therapy may comprise administering a therapy to thesubject. Determining the therapy may comprise modifying a therapeuticregimen. Modifying the therapeutic regimen may comprise terminating atherapeutic regimen. Modifying the therapeutic regimen may compriseadjusting a dosage of the therapy. Modifying the therapeutic regimen maycomprise adjusting a frequency of the therapy. The therapeutic regimenmay be modified based on a change in the quantity of the ctDNA. Thedosage of the therapy may be increased in response to an increase in thequantity of the ctDNA. The dosage of the therapy may be decreased inresponse to a decrease in the quanitity of the ctDNA. The frequency ofthe therapy may be increased in response to an increase in the quantityof the ctDNA. The frequency of the therapy may be decreased in responseto a decrease in the quanitity of ctDNA.

Alternatively, the method may comprise (a) obtaining sequenceinformation of cell-free genomic DNA derived from a sample from asubject, wherein the sequence information is derived from regions thatare mutated in at least 80% of a population of subjects afflicted with acondition; and (b) determining a therapeutic regimen of a condition inthe subject based on the sequence information. The regions that aremutated may comprise a total size of less than 1.5 Mb of the genome. Theregions that are mutated may comprise a total size of less than 1 Mb ofthe genome. The regions that are mutated may comprise a total size ofless than 500 kb of the genome. The regions that are mutated maycomprise a total size of less than 350 kb of the genome. The regionsthat are mutated may comprise a total size between 100 kb-300 kb of thegenome. The sequence information may be derived from 2 or more regions.The sequence may be derived from 10 or more regions. The sequence may bederived from 50 or more regions. The population of subjects afflictedwith the condition may be subjects from one or more databases. The oneor more databases may comprise The Cancer Genome Atlas (TCGA). Thesequence information may comprise information pertaining to at least onemutation that may be present in at least about 60% of the population ofsubjects afflicted with the condition. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 70% of the population of subjects afflictedwith the condition. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 80% of the population of subjects afflicted with the condition.The sequence information may comprise information pertaining to at leastone mutation that may be present in at least about 90% of the populationof subjects afflicted with the condition. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 95% of the population of subjects afflictedwith the condition. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 99% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 85% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 90% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 95% of the population of subjects afflicted with the condition.The sequence information may be derived from regions that are mutated inat least 99% of the population of subjects afflicted with the condition.Obtaining sequence information may comprise sequencing noncodingregions. The noncoding regions may comprise one or more 1ncRNA, snoRNA,siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA,uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes,GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.Obtaining sequence information may comprise sequencing protein codingregions. The protein coding regions may comprise one or more exons,introns, untranslated regions, or a combination thereof. In someinstances, at least one of the regions does not comprise KRAS or EGFR.In some instances, at least two of the regions do not comprise KRAS andEGFR. In some instances, at least one of the regions does not compriseKRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, atleast two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF,EZH2, or BRCA1. In some instances, at least three of the regions do notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least four of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. The method may further comprise detectingmutations in the regions based on the sequencing information.Determining the therapeutic regimen may be based on the detection of themutations. The condition may be a cancer. The cancer may be a solidtumor. The solid tumor may be non-small cell lung cancer (NSCLC). Thecancer may be a breast cancer. The breast cancer may be a BRCA1 cancer.The cancer may be a lung cancer, colorectal cancer, prostate cancer,ovarian cancer, esophageal cancer, breast cancer, lymphoma, or leukemia.

Further disclosed herein are methods for diagnosing, prognosing, ordetermining a therapeutic regimen for a subject afflicted with orsusceptible of having a cancer. The method may comprise (a) obtainingsequence information for selected regions of genomic DNA from acell-free DNA sample from the subject; (b) using the sequenceinformation to determine the presence or absence of one or moremutations in the selected regions, wherein at least 70% of a populationof subjects afflicted with the cancer have mutation(s) in the regions;and (c) providing a report with a diagnosis, prognosis or treatmentregimen to the subject, based on the presence or absence of the one ormore mutations. The selected regions may comprise a total size of lessthan 1.5 Mb of the genome. The selected regions may comprise a totalsize of less than 1 Mb of the genome. The selected regions may comprisea total size of less than 500 kb of the genome. The selected regionsmutated may comprise a total size of less than 350 kb of the genome. Theselected regions may comprise a total size between 100 kb-300 kb of thegenome. The sequence information may be derived from 2 or more selectedregions. The sequence may be derived from 10 or more selected regions.The sequence may be derived from 50 or more selected regions. Thepopulation of subjects afflicted with the cancer may be subjects fromone or more databases. The one or more databases may comprise The CancerGenome Atlas (TCGA). The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 60% of the population of subjects afflicted with the cancer. Thesequence information may comprise information pertaining to at least onemutation that may be present in at least about 70% of the population ofsubjects afflicted with the cancer. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 80% of the population of subjects afflictedwith the cancer. The sequence information may comprise informationpertaining to at least one mutation that may be present in at leastabout 90% of the population of subjects afflicted with the cancer. Thesequence information may comprise information pertaining to at least onemutation that may be present in at least about 95% of the population ofsubjects afflicted with the cancer. The sequence information maycomprise information pertaining to at least one mutation that may bepresent in at least about 99% of the population of subjects afflictedwith the cancer. The sequence information may be derived from regionsthat are mutated in at least 85% of the population of subjects afflictedwith the cancer. The sequence information may be derived from regionsthat are mutated in at least 90% of the population of subjects afflictedwith the cancer. The sequence information may be derived from regionsthat are mutated in at least 95% of the population of subjects afflictedwith the cancer. The sequence information may be derived from regionsthat are mutated in at least 99% of the population of subjects afflictedwith the cancer. Obtaining sequence information may comprise sequencingnoncoding regions. The noncoding regions may comprise one or morelncRNA, snoRNA, siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA,snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs,pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combinationthereof. Obtaining sequence information may comprise sequencing proteincoding regions. The protein coding regions may comprise one or moreexons, introns, untranslated regions, or a combination thereof. In someinstances, at least one of the regions does not comprise KRAS or EGFR.In some instances, at least two of the regions do not comprise KRAS andEGFR. In some instances, at least one of the regions does not compriseKRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, atleast two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF,EZH2, or BRCA1. In some instances, at least three of the regions do notcomprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In someinstances, at least four of the regions do not comprise KRAS, EGFR, p53,PIK3CA, BRAF, EZH2, or BRCA1. The detection of at least 3 mutations maybe indicative of an outcome of the cancer. The detection of one or moremutations in three or more regions may be indicative of an outcome ofthe cancer. The cancer may be non-small cell lung cancer (NSCLC). Thecancer may be a breast cancer. The breast cancer may be a BRCA1 cancer.The cancer may be a lung cancer, colorectal cancer, prostate cancer,ovarian cancer, esophageal cancer, breast cancer, lymphoma, or leukemia.The method of diagnosing or prognosing the cancer has a sensitivity ofat least 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%,96%, 97%, 98%, or 99%. The method of diagnosing or prognosing the cancerhas a specificity of at least 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The method mayfurther comprise administering a therapeutic drug to the subject. Themethod may further comprise modifying a therapeutic regimen. Modifyingthe therapeutic regimen may comprise terminating the therapeuticregimen. Modifying the therapeutic regimen may comprise increasing adosage or frequency of the therapeutic regimen. Modifying thetherapeutic regimen may comprise decreasing a dosage or frequency of thetherapeutic regimen. Modifying the therapeutic regimen may comprisestarting the therapeutic regimen.

In some embodiment, the method further comprises selecting a therapeuticregimen based on the analysis. In an embodiment, the method furthercomprises determining a treatment course for the subject based on theanalysis. In such embodiments, the presence of tumor cells in anindividual, including an estimation of tumor load, provides informationto guide clinical decision making, both in terms of institution of andescalation of therapy as well as in the selection of the therapeuticagent to which the patient is most likely to exhibit a robust response.

The information obtained by CAPP-seq can be used to (a) determine typeand level of therapeutic intervention warranted (e.g. more versus lessaggressive therapy, monotherapy versus combination therapy, type ofcombination therapy), and (b) to optimize the selection of therapeuticagents. With this approach, therapeutic regimens can be individualizedand tailored according to the specificity data obtained at differenttimes over the course of treatment, thereby providing a regimen that isindividually appropriate. In addition, patient samples can be obtainedat any point during the treatment process for analysis.

The therapeutic regimen may be selected based on the specific patientsituation. Where CAPP-seq is used as an initial diagnosis, a samplehaving a positive finding for the presence of ctDNA can indicate theneed for additional diagnostic tests to confirm the presence of a tumor,and/or initiation of cytoreductive therapy, e.g. administration ofchemotherapeutic drugs, administration of radiation therapy, and/orsurgical removal of tumor tissue.

Further disclosed herein are methods for assessing tumor burden in asubject. The method may comprise (a) obtaining sequence information oncell-free nucleic acids derived from a sample from the subject; (b)using a computer readable medium to determine quantities of circulatingtumor DNA (ctDNA) in the sample; (c) assessing tumor burden based on thequantities of ctDNA; and (d) reporting the tumor burden to the subjector a representative of the subject. Determining quantities of ctDNA maycomprise determining absolute quantities of ctDNA. Determiningquantities of ctDNA may comprise determining relative quantities ofctDNA. Determining quantities of ctDNA may be performed by countingsequence reads pertaining to the ctDNA. Determining quantities of ctDNAmay be performed by quantitative PCR. Determining quantities of ctDNAmay be performed by digital PCR. Determining quantities of ctDNA may beperformed by molecular barcoding of the ctDNA. Molecular barcoding ofthe ctDNA may comprise attaching barcodes to one or more ends of thectDNA. The barcode may comprise a random sequence. Two or more barcodesmay comprise two or more different random sequences. The barcode maycomprise an adaptor sequence. Two or more barcodes may comprise the sameadaptor sequence. The barcode may comprise a primer sequence. Two ormore barcodes may comprise the same primer sequence. The primer sequencemay be a PCR primer sequence. The primer sequence may be a sequencingprimer. Attaching the barcodes to one or more ends of the ctDNA maycomprise ligating the barcodes to the one or more ends of the ctDNA. Thesequence information may comprise information related to one or moregenomic regions. The sequence information may comprise informationrelated to at least 10, 20, 30, 40, 100, 200, 300 genomic regions. Thegenomic regions may comprise genes, exonic regions, intronic regions,untranslated regions, non-coding regions or a combination thereof. Thegenomic regions may comprise two or more of exonic regions, intronicregions, and untranslated regions. The genomic regions may comprise atleast one exonic region and at least one intronic region. At least 5% ofthe genomic regions may comprise intronic regions. At least about 20% ofthe genomic regions may comprise exonic regions. The genomic regions maycomprise less than 1.5 megabases (Mb) of the genome. The genomic regionsmay comprise less than 1 Mb of the genome. The genomic regions maycomprise less than 500 kilobases (kb) of the genome. The genomic regionsmay comprise less than 350 kb of the genome. The genomic regions maycomprise between 100 kb to 300 kb of the genome. The sequenceinformation may comprise information pertaining to 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 15, 20 or more genomic regions from a selector set comprisinga plurality of genomic regions. The sequence information may compriseinformation pertaining to 25, 30, 40, 50, 60, 70, 80, 90, 100 or moregenomic regions from a selector set comprising a plurality of genomicregions. The sequence information may comprise information pertaining toa plurality of genomic regions. The plurality of genomic regions may bebased on a selector set comprising genomic regions comprising one ormore mutations present in one or more subjects from a population ofcancer subjects. At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%,45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of theplurality of genomic regions may be based on a selector set comprisinggenomic regions comprising one or more mutations present in one or moresubjects from a population of cancer subjects. The total size of thegenomic regions of the selector set may comprise less than 1.5 megabases(Mb), 1 Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150kb of the genome. The total size of the genomic regions of the selectorset may be between 100 kb to 300 kb of the genome. The selector set maycomprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70,80, 90, 100 or more genomic regions selected from Table 2. Obtainingsequence information may comprise performing massively parallelsequencing. Massively parallel sequencing may be performed on a subsetof a genome of the cell-free nucleic acids from the sample. The subsetof the genome may comprise less than 1.5 megabases (Mb), 1 Mb, 500kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome.The subset of the genome may comprise between 100 kb to 300 kb of thegenome. The method may comprise obtaining sequencing information ofcell-free DNA samples from two or more samples from the subject. The twoor more samples are the same type of sample. The two or more samples aretwo different types of sample. The two or more samples are obtained fromthe subject at the same time point. The two or more samples are obtainedfrom the subject at two or more time points. Determining the quantitiesof ctDNA may comprise detecting one or more SNVs, indels, fusions,breakpoints, structural variants, variable number of tandem repeats,hypervariable regions, minisatellites, dinucleotide repeats,trinucleotide repeats, tetranucleotide repeats, simple sequence repeats,or a combination thereof in selected regions of the subject's genome.Determining the quantities of ctDNA may comprise detecting one or moreof SNVs, indels, copy number variants, and rearrangements in selectedregions of the subject's genome. Determining the quantities of ctDNA maycomprise detecting two or more of SNVs, indels, copy number variants,and rearrangements in selected regions of the subject's genome.Determining the quantities of ctDNA may comprise detecting at least oneSNV, indel, copy number variant, and rearrangement in selected regionsof the subject's genome. Determining the quantities of ctDNA does notinvolve performing digital PCR (dPCR). Determining the quantities ofctDNA may comprise applying an algorithm to the sequence information todetermine a quantity of one or more genomic regions from a selector set.The selector set may comprise a plurality of genomic regions comprisingone or more mutations present in one or more cancer subjects from apopulation of cancer subjects. The selector set may comprise a pluralityof genomic regions comprising one or more mutations present in at leastabout 60% of cancer subjects from population of cancer subjects. Therepresentative of the subject may be a healthcare provider. Thehealthcare provider may be a nurse, physician, medical technician, orhospital personnel. The representative of the subject may be a familymember of the subject. The representative of the subject may be a legalguardian of the subject.

Further disclosed herein are methods for determining a disease state ofa cancer in a subject. The method may comprise (a) obtaining a quantityof circulating tumor DNA (ctDNA) in a sample from the subject; (b)obtaining a volume of a tumor in the subject; and (c) determining adisease state of a cancer in the subject based on a ratio of thequantity of ctDNA to the volume of the tumor. A high ctDNA to volumeratio may be indicative of radiographically occult disease. A low ctDNAto volume ratio may be indicative of non-malignant state. Obtaining thevolume of the tumor may comprise obtaining an image of the tumor.Obtaining the volume of the tumor may comprise obtaining a CT scan ofthe tumor. Obtaining the quantity of ctDNA may comprise digital PCR.Obtaining the quantity of ctDNA may comprise obtaining sequencinginformation on the ctDNA. The sequencing information may compriseinformation relating to one or more genomic regions based on a selectorset. Obtaining the quantity of ctDNA may comprise hybridization of thectDNA to an array. The array may comprise a plurality of probes forselective hybridization of one or more genomic regions based on aselector set. The selector set may comprise one or more genomic regionsfrom Table 2. The selector set may comprise one or more genomic regionscomprising one or more mutations, wherein the one or more mutations arepresent in a population of subjects suffering from a cancer. Theselector set may comprise a plurality of genomic regions comprising aplurality of mutations, wherein the plurality of mutations are presentin at least 60% of a population of subjects suffering from a cancer.

In some embodiments, the ctDNA content in an individual's blood, orblood derivative, sample is determined at one or more time points,optionally in conjunction with a therapeutic regimen. The presence ofthe ctDNA correlates with tumor burden, and is useful in monitoringresponse to therapy, monitoring residual disease, monitoring for thepresence of metastases, monitoring total tumor burden, and the like.Although not required, for some methods CAPP-Seq may be performed inconjunction with tumor imaging methods, e.g. PET/CT scans and the like.Where CAPP-seq is used to estimate tumor burden or residual disease,increased presence of tumor cells over time indicates a need to increasethe therapy by escalating dose, selection of agent, etc.Correspondingly, where CAPP-seq shows no evidence of residual disease, apatient may be taken off therapy, or put on a lowered dose.

CAPP-seq can also be used in clinical trials for new drugs, to determinethe efficacy of treatment for a cancer of interest, where a decrease intumor burden is indicative of efficacy and increased tumor burden isindicative of a lack of efficacy.

The cancer of interest may be specific for a cancer, for examplenon-small cell carcinoma, endometrioid uterine carcinoma, etc.; or maybe generic for a class of cancers, e.g. epithelial cancers (carcinomas);sarcomas; lymphomas; melanomas; gliomas; teratomas; etc.; or subgenus,e.g. adenocarcinoma; squamous cell carcinoma; and the like.

The term “diagnosis” may refer to the identification of a molecular orpathological state, disease or condition, such as the identification ofa molecular subtype of breast cancer, prostate cancer, or other type ofcancer.

The term “prognosis” may refer to the prediction of the likelihood ofcancer-attributable death or progression, including recurrence,metastatic spread, and drug resistance, of a neoplastic disease, such asovarian cancer. The term “prediction” may refer to the act offoretelling or estimating, based on observation, experience, orscientific reasoning. In one example, a physician may predict thelikelihood that a patient will survive, following surgical removal of aprimary tumor and/or chemotherapy for a certain period of time withoutcancer recurrence.

The terms “treatment,” “treating,” and the like, may refer toadministering an agent, or carrying out a procedure, for the purposes ofobtaining an effect. The effect may be prophylactic in terms ofcompletely or partially preventing a disease or symptom thereof and/ormay be therapeutic in terms of effecting a partial or complete cure fora disease and/or symptoms of the disease. “Treatment,” as used herein,may include treatment of a tumor in a mammal, particularly in a human,and includes: (a) preventing the disease or a symptom of a disease fromoccurring in a subject which may be predisposed to the disease but hasnot yet been diagnosed as having it (e.g., including diseases that maybe associated with or caused by a primary disease; (b) inhibiting thedisease, e.g., arresting its development; and (c) relieving the disease,e.g., causing regression of the disease.

DEFINITIONS

A number of terms conventionally used in the field of cell culture areused throughout the disclosure. In order to provide a clear andconsistent understanding of the specification and claims, and the scopeto be given to such terms, the following definitions are provided.

It is to be understood that this invention is not limited to theparticular methodology, protocols, cell lines, animal species or genera,and reagents described, as such may vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to limit the scope ofthe present invention, which will be limited only by the appendedclaims.

As used herein the singular forms “a”, “an”, and “the” include pluralreferents unless the context clearly dictates otherwise. Thus, forexample, reference to “a cell” may include a plurality of such cells andreference to “the culture” may include reference to one or more culturesand equivalents thereof known to those skilled in the art, and so forth.All technical and scientific terms used herein have the same meaning ascommonly understood to one of ordinary skill in the art to which thisinvention belongs unless clearly indicated otherwise.

“Measuring” or “measurement” in the context of the present teachings mayrefer to determining the presence, absence, quantity, amount, oreffective amount of a substance in a clinical or subject-derived sample,including the presence, absence, or concentration levels of suchsubstances, and/or evaluating the values or categorization of asubject's clinical parameters based on a control.

Unless otherwise apparent from the context, all elements, steps orfeatures of the invention can be used in any combination with otherelements, steps or features.

General methods in molecular and cellular biochemistry can be found insuch standard textbooks as Molecular Cloning: A Laboratory Manual, 3rdEd. (Sambrook et al., Harbor Laboratory Press 2001); Short Protocols inMolecular Biology, 4th Ed. (Ausubel et al. eds., John Wiley & Sons1999); Protein Methods (Bollag et al., John Wiley & Sons 1996); NonviralVectors for Gene Therapy (Wagner et al. eds., Academic Press 1999);Viral Vectors (Kaplift & Loewy eds., Academic Press 1995); ImmunologyMethods Manual (I. Lefkovits ed., Academic Press 1997); and Cell andTissue Culture: Laboratory Procedures in Biotechnology (Doyle &Griffiths, John Wiley & Sons 1998). Reagents, cloning vectors, and kitsfor genetic manipulation referred to in this disclosure may be availablefrom commercial vendors such as BioRad, Stratagene, Invitrogen,Sigma-Aldrich, and ClonTech.

The invention has been described in terms of particular embodimentsfound or proposed by the present inventor to comprise preferred modesfor the practice of the invention. It will be appreciated by those ofskill in the art that, in light of the present disclosure, numerousmodifications and changes can be made in the particular embodimentsexemplified without departing from the intended scope of the invention.Due to biological functional equivalency considerations, changes can bemade in protein structure without affecting the biological action inkind or amount. All such modifications are intended to be includedwithin the scope of the appended claims.

The terms “subject,” “individual,” and “patient” are usedinterchangeably herein and may refer to a mammal being assessed fortreatment and/or being treated. In an embodiment, the mammal is a human.The terms “subject,” “individual,” and “patient” may encompass, withoutlimitation, individuals having cancer or suspected of having cancer.Subjects may be human, but also include other mammals, particularlythose mammals useful as laboratory models for human disease, e.g. mouse,rat, etc. Also included are mammals such as domestic and other speciesof canines, felines, and the like.

The terms “cancer,” “neoplasm,” and “tumor” are used interchangeablyherein and may refer to cells which exhibit autonomous, unregulatedgrowth, such that they exhibit an aberrant growth phenotypecharacterized by a significant loss of control over cell proliferation.Cells of interest for detection, analysis, or treatment in the presentapplication may include, but are not limited to, precancerous (e.g.,benign), malignant, pre-metastatic, metastatic, and non-metastaticcells. Cancers of virtually every tissue are known. The phrase “cancerburden” may refer to the quantum of cancer cells or cancer volume in asubject. Reducing cancer burden accordingly may refer to reducing thenumber of cancer cells or the cancer volume in a subject. The term“cancer cell” as used herein may refer to any cell that is a cancer cellor is derived from a cancer cell, e.g. clone of a cancer cell. Manytypes of cancers are known to those of skill in the art, including solidtumors such as carcinomas, sarcomas, glioblastomas, melanomas,lymphomas, myelomas, etc., and circulating cancers such as leukemias.Examples of cancer include, but are not limited to, ovarian cancer,breast cancer, colon cancer, lung cancer, prostate cancer,hepatocellular cancer, gastric cancer, pancreatic cancer, cervicalcancer, ovarian cancer, liver cancer, bladder cancer, cancer of theurinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, headand neck cancer, and brain cancer.

The “pathology” of cancer may include, but it not limited to, allphenomena that compromise the well-being of the patient. This includes,without limitation, abnormal or uncontrollable cell growth, metastasis,interference with the normal functioning of neighboring cells, releaseof cytokines or other secretory products at abnormal levels, suppressionor aggravation of inflammatory or immunological response, neoplasia,premalignancy, malignancy, invasion of surrounding or distant tissues ororgans, such as lymph nodes, etc.

As used herein, the terms “cancer recurrence” and “tumor recurrence,”and grammatical variants thereof, may refer to further growth ofneoplastic or cancerous cells after diagnosis of cancer. Particularly,recurrence may occur when further cancerous cell growth occurs in thecancerous tissue. “Tumor spread,” similarly, may occur when the cells ofa tumor disseminate into local or distant tissues and organs; thereforetumor spread may encompass tumor metastasis. “Tumor invasion” may occurwhen the tumor growth spreads out locally to compromise the function ofinvolved tissues by compression, destruction, and/or prevention ofnormal organ function.

As used herein, the term “metastasis” may refer to the growth of acancerous tumor in an organ or body part, which is not directlyconnected to the organ of the original cancerous tumor. Metastasis mayinclude micrometastasis, which is the presence of an undetectable amountof cancerous cells in an organ or body part which is not directlyconnected to the organ of the original cancerous tumor. Metastasis canalso be defined as several steps of a process, such as the departure ofcancer cells from an original tumor site, and migration and/or invasionof cancer cells to other parts of the body.

As used herein, DNA, RNA, nucleic acids, nucleotides, oligonucleotides,polynucleotides may be used interchangeably. Unless explicitly statedotherwise, the term DNA encompasses any type of nucleic acid (e.g., DNA,RNA, DNA/RNA hybrids, and analogues thereof). In instances in which RNAis used in the methods disclosed herein, the methods may furthercomprise reverse transcription of the RNA to produce a complementary DNA(cDNA) or DNA copy.

All publications and patent applications cited in this specification areherein incorporated by reference as if each individual publication orpatent application were specifically and individually indicated to beincorporated by reference.

The present invention has been described in terms of particularembodiments found or proposed by the present inventor to comprisepreferred modes for the practice of the invention. It will beappreciated by those of skill in the art that, in light of the presentdisclosure, numerous modifications and changes can be made in theparticular embodiments exemplified without departing from the intendedscope of the invention. For example, due to codon redundancy, changescan be made in the underlying DNA sequence without affecting the proteinsequence. In another example, due to similarities in DNA and RNA, themethods, compositions, and systems may be equally applicable to alltypes of nucleic acids (e.g., DNA, RNA, DNA/RNA hybrids, and analoguesthereof). Moreover, due to biological functional equivalencyconsiderations, changes can be made in protein structure withoutaffecting the biological action in kind or amount. All suchmodifications are intended to be included within the scope of theappended claims.

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the present invention, and are not intended to limit thescope of what the inventors regard as their invention nor are theyintended to represent that the experiments below are all or the onlyexperiments performed. Efforts have been made to ensure accuracy withrespect to numbers used (e.g. amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

EXAMPLES Example 1 An Ultrasensitive Method for Quantitating CirculatingTumor DNA with Broad Patient Coverage

Circulating tumor DNA (ctDNA) represents a promising biomarker fornoninvasive detection of disease burden and monitoring of recurrence.However, existing ctDNA detection methods are limited by sensitivity, afocus on small numbers of mutations, and/or the need forpatient-specific optimization. To address these shortcomings, CAncerPersonalized Profiling by Deep Sequencing (CAPP-Seq) was developed, aneconomical and highly sensitive method for quantifying ctDNA in plasmain nearly every patient. We implemented CAPP-Seq for non-small cell lungcancer (NSCLC) with a design that identified mutations in >95% oftumors, simultaneously detecting point mutations, insertions/deletions,copy number variants, and rearrangements. When tumor mutation profileswere known, we detected ctDNA in 100% of pre-treatment plasma samplesfrom stages II-IV NSCLC and 50% of samples from stage I NSCLC, with aspecificity of 95% for mutant allele fractions down to ˜0.02%. Absolutequantities of ctDNA were significantly correlated with tumor volume.Furthermore, ctDNA levels in post-treatment samples helped distinguishbetween residual disease and treatment-related imaging changes andprovided earlier response assessment than radiographic approaches.Finally, we explored the utility of this method for biopsy-free tumorgenotyping and cancer screening. CAPP-Seq can be routinely appliedclinically to detect and monitor diverse malignancies, thus facilitatingpersonalized cancer therapy. Here we demonstrate the technicalperformance and explore the clinical utility of CAPP-Seq in patientswith early and advanced stage NSCLC.

Design of a CAPP-Seq Selector for NSCLC.

For the initial implementation of CAPP-Seq we focused on NSCLC, althoughour approach can be used for any cancer for which recurrent mutationshave been identified. We employed a multi-phase approach to design anNSCLC-specific selector, aiming to identify genomic regions recurrentlymutated in this disease (FIG. 1 b, Table 1). We began by including exonscovering recurrent mutations in potential driver genes from theCatalogue of Somatic Mutations in Cancer (COSMIC) database as well asother sources (e.g. KRAS, EGFR, TP53). Next, using whole exomesequencing (WES) data from 407 NSCLC patients profiled by The CancerGenome Atlas (TCGA), we applied an iterative algorithm to maximize thenumber of missense mutations per patient while minimizing selector size.Our approach relied on a recurrence index that identified known drivermutations as well as uncharacterized genes that are frequently mutatedand are therefore likely to be involved in NSCLC pathogenesis (FIG. 7and Table 2).

Approximately 8% of NSCLCs harbor clinically actionable rearrangementsinvolving the receptor tyrosine kinases, ALK, ROS1 and RET. Thesestructural aberrations, which are clinically actionable because they aretargets of pharmacologic inhibitors, tend to disproportionately occur inyounger patients with significantly less smoking history and whosetumors harbor fewer somatic alterations than most other patients withNSCLC. To utilize the personalized nature and lower false detection rateinherent in the unique junctional sequences of structuralrearrangements, we included the introns and exons spanning recurrentfusion breakpoints in these genes in the final design phase (FIG. 1 b).To detect fusions in tumor and plasma DNA, we developed abreakpoint-mapping algorithm called FACTERA (FIG. 8). Application ofFACTERA to next generation sequencing (NGS) data from 2 NSCLC cell linesknown to harbor fusions with previously uncharacterized breakpointsreadily identified the breakpoints at nucleotide resolution and thesewere independently confirmed in both cases (FIG. 9).

Collectively, the NSCLC selector design targets 521 exons and 13 intronsfrom 139 recurrently mutated genes, in total covering ˜125 kb (FIG. 1b). Within this small target (0.004% of the human genome), the selectoridentifies a median of 4 point mutations and covers 96% of patients withlung adenocarcinoma or squamous cell carcinoma. To validate the numberof mutations covered per tumor, we examined the selector region in WESdata from an independent cohort of 183 lung adenocarcinoma patients. Theselector covered 88% of patients with a median of 4 SNVs per patient,thus validating our selector design algorithm (P<1.0×10⁻⁶; FIG. 1 c).When compared to randomly sampling the exome, regions targeted by theNSCLC selector captured ˜4-fold more mutations per patient (at themedian, FIG. 1 c). Due to similarities in key oncogenic machinery acrosscancers, the NSCLC selector performs favorably on other carcinomas.Indeed, the selector successfully captured 99% of colon, 98% of rectal,and 97% of endometrioid uterine carcinomas, with a median of 12, 7, and3 mutations per patient, respectively (FIG. 1 d). This demonstrates thevalue of targeting hundreds of recurrently mutated genomic regions andshows that a single selector can be designed to simultaneously coverrecurrent mutations for multiple malignancies.

Methodological Optimization and Performance Assessment.

We performed deep sequencing with the NSCLC selector to achieve ˜10,000×coverage (pre-duplication removal, ˜10-12 samples per lane), andprofiled a total of 90 samples, including 2 NSCLC cell lines, 17 primarytumor biopsies and matched peripheral blood leukocyte (PBL) specimens,and 40 plasma samples from 18 human subjects, including 5 healthy adultsand 13 patients with NSCLC before and after various cancer therapies(Tables 3, 20 and 21). To assess and optimize selector performance, wefirst applied it to cfDNA purified from healthy control plasma,observing efficient and uniform capture of genomic DNA (Tables 3, 20 and21). Sequenced cfDNA fragments had a median length of ˜170 bp (FIG. 2a), closely corresponding to the length of DNA contained within achromatosome. To optimize library preparation from small quantities ofcfDNA we explored a variety of modifications to the ligation andpost-ligation amplification steps including temperature, incubationtime, DNA polymerase, and PCR purification. The optimized protocolincreased recovery efficiency by >300% and decreased bias for librariesconstructed from as little as 4 ng of cfDNA (FIGS. 10, 11, and 12).Consequently, fluctuations in sequencing depth were minimal (FIG. 2b,c).

The detection limit of CAPP-Seq is affected by (i) the input number andrecovery rate of cfDNA molecules, (ii) sample cross-contamination, (iii)potential allelic bias in the capture reagent, and (iv) PCR orsequencing errors (e.g., “technical” background). We examined each ofthese elements in turn to better understand their potential impact onCAPP-Seq sensitivity. First, by comparing the number of input DNAmolecules per sample with estimates of library complexity (FIG. 13 a),we calculated a cfDNA molecule recovery rate of ≧49% (Tables 3, 20 and21). This was in agreement with molecule recovery efficienciescalculated using post-PCR mass yields (FIG. 13 b). Second, by analyzingpatient-specific homozygous SNPs across samples, we foundcross-contamination of ˜0.06% in multiplexed cfDNA (FIG. 14). While toolow to affect ctDNA detection in most applications, we excluded anytumor-derived SNV from further analysis if found as a germline SNP inanother profiled patient. To analyze possible capture bias, we nextevaluated the allelic skew in heterozygous SNPs (single nucleotidepolymorphism) within patient PBL (peripheral blood lymphocyte) samples.We observed a median heterozygous allele fraction of 51% (FIG. 15),indicating minimal bias toward capture of reference alleles. Finally, weanalyzed the distribution of non-reference alleles across the selectorfor the 40 cfDNA samples, excluding tumor-derived SNVs and germline SNPs(FIG. 2 d). We found mean and median technical background rates of0.006% and 0.0003%, respectively (FIG. 2 d), both considerably lowerthan previously reported NGS-based methods for ctDNA analysis.

In addition to technical background, mutant cfDNA could be present inthe absence of cancer due to contributions from pre-neoplastic cellsfrom diverse tissues, and such “biological” background may impactsensitivity. We hypothesized that biological background, if present,would be particularly high for recurrently mutated positions in knowncancer driver genes and therefore analyzed mutation rates of 107selected cancer-associated SNVs in all 40 plasma samples, excludingsomatic mutations found in a patient's tumor. Though the medianfractional abundance was comparable to the global selector background(˜0%), the mean was marginally higher at ˜0.01% (FIG. 2 e). Strikingly,one mutation (TP53 R175H) was detected at a median frequency of ˜0.18%across all cfDNA samples, including patients and healthy subjects (FIG.2 f). Since this allele is significantly above global background(P<0.01; FIG. 2 f), we hypothesize that it reflects true biologicalbackground and thus excluded it as a potential reporter. To addressbackground more generally, we also normalized for allele-specificdifferences in background rate when assessing the significance of ctDNAdetection. As a result, we found that biological background is not asignificant factor for ctDNA quantitation at detection limits above˜0.01%.

Next, we empirically benchmarked the allele frequency detection limitand linearity of CAPP-Seq by spiking defined concentrations offragmented genomic DNA from a NSCLC cell line into cfDNA from a healthyindividual (FIG. 2 g) or into genomic DNA from a second NSCLC line (FIG.16 a). Defined inputs of NSCLC DNA were accurately detected atfractional abundances between 0.025% and 10% with high linearity(R²≧0.994). Analyses of the influence of the number of SNP reporters onerror metrics showed only marginal improvements above a threshold of 4reporters (FIG. 2 h,i, FIG. 16 b,c), equivalent to the median number ofSNVs per NSCLC tumor identified by the selector. We also tested whetherfusion breakpoints, indels, and CNVs could serve as linear reporters andfound that the fractional abundance of these mutation types correlatedhighly with expected concentrations (R²≧0.97; FIG. 16 d).

Identification of somatic mutations in NSCLC patients. Having designed,optimized, and assessed the technical performance of CAPP-Seq, weapplied it to the discovery of somatic mutations in tumors collectedfrom a diverse group of 17 NSCLC patients (Table 1 and Table 19). Totest the utility of CAPP-Seq for identifying structural rearrangements,which are more frequently seen in tumors from nonsmokers, we included 6patients with clinically confirmed fusions. These translocations servedas positive controls, along with SNVs in other tumors previouslyidentified by clinical assays (Table 19). Tumor samples includedformalin fixed surgical or biopsy specimens and pleural fluid containingmalignant cells. At a mean sequencing depth of ˜5,000× (pre-duplicateremoval) in tumor and paired germline samples (Tables 3, 20 and 21), wedetected 100% of previously identified SNVs and fusions (7 and 8,respectively) and discovered many additional somatic variants (Table 1and Table 19). Moreover, partner genes and base-pair resolutionbreakpoints were characterized for each of the 8 rearrangements (FIG.17). Tumors containing fusions were almost exclusively from neversmokers and, as expected, contained fewer SNVs than those lackingfusions (FIG. 18). Excluding patients with fusions (<10% of the TCGAdesign cohort), we identified a median of 6 SNVs (3 missense) perpatient (Table 1), in line with our selector design-stage predictions(FIG. 1 b-c).

Sensitivity and Specificity.

Next, we assessed the sensitivity and specificity of CAPP-Seq fordisease monitoring and minimal residual disease detection, using plasmasamples from 5 healthy controls and 35 serial samples collected from 13NSCLC patients, all but one of whom had pre- and post-treatment samplesavailable (Table 1; Table 5). CAPP-Seq was used to measure tumor burdenacross the entire grid of plasma cfDNA samples (13 patient-specific setsof somatic reporters across 40 plasma samples, or 520 pairs), with anapproach that integrates information content across multiple instancesand classes of somatic mutations to increase sensitivity andspecificity. Using ROC analysis, we achieved a maximal sensitivity andspecificity of 85% and 95% (AUC=0.95), respectively, for all pre-treatedtumors and healthy controls. Sensitivity among stage I tumors was 50%and among stage II-IV patients was 100% with a specificity of 96% (FIG.3 a,b). Moreover, when considering both pre and post-treatment samplesin an ROC analysis, CAPP-Seq exhibited robust performance, with AUCvalues of 0.89 for all stages and 0.91 for stages II-IV (P<0.0001; FIG.19). Furthermore, by adjusting the ctDNA detection index, we couldincrease specificity up to 98% while still capturing ⅔ of allcancer-positive samples and ¾ of stage II-IV cancer-positive samples(FIG. 20). This indicates that our approach could be tuned to deliver adesired sensitivity and specificity depending on the application inquestion and that CAPP-Seq can achieve robust assessment of tumor burdenin NSCLC patients.

Monitoring of NSCLC Tumor Burden in Plasma Samples.

We next asked whether significantly detectable levels of ctDNA correlatewith radiographically measured tumor volume and clinical response totherapy. Fractions of tumor-derived DNA detected in plasma by SNV and/orindel reporters ranged from ˜0.02% to 3.2% (Table 1), with a median of˜0.1% in pre-treatment samples. Moreover, absolute levels of ctDNA inpre-treatment plasma were significantly correlated with tumor volume asmeasured by computed tomography (CT) and positron emission tomography(PET) imaging (R²=0.89, P=0.0002; FIG. 3 c).

To determine whether ctDNA concentrations reflect disease burden inlongitudinal samples, we analyzed plasma cfDNA from three patients withhigh disease burden who underwent several rounds of therapy formetastatic NSCLC, including surgery, radiotherapy, chemotherapy, andtyrosine kinase inhibitors (FIG. 4 a-c). As in pre-treatment samples,ctDNA levels were highly correlated with tumor volumes during therapy(R²=0.95 for P15; R²=0.85 for P9). In a never-smoker (P6), we detected 3SNVs and a KIF5B-ALK fusion, and both mutation types were simultaneouslydetectable in plasma cfDNA and behaved comparably in response toCrizotinib therapy (FIG. 4 c). In all 3 patients, this behavior wasobserved whether the mutation type measured was a collection of SNVs andan indel (P15, FIG. 4 a), multiple fusions (P9, FIG. 4 b), or SNVs and afusion (P6, FIG. 4 c), validating the utility of diverse tumor-derivedsomatic lesions. Of note, in one patient (P9) we identified both aclassic EML4-ALK fusion and two previously unreported fusions involvingROS1: FYN-ROS1 and ROS1-MKX (FIG. 17). All fusions were confirmed byqPCR amplification of genomic DNA and were independently recovered inplasma samples (Table 5). While the potential function of these novelROS1 fusions is unknown, to the best of our knowledge this is the firstobservation of ROS1 and ALK fusions in the same NSCLC patient.

The NSCLC selector was designed to detect multiple SNVs per tumor and ifpresent, more than 1 type of mutation per tumor. In one patient's tumor(P5), this design allowed us to identify a dominant clone with anactivating EGFR mutation as well as a subclone with an EGFR T790M“gatekeeper” mutation. The ratio between clones was identical in a tumorbiopsy and simultaneously sampled plasma (FIG. 4 d), demonstrating thatby detecting multiple reporters per tumor, our method is useful fordetecting and quantifying clinically relevant subclones.

Having validated the performance of CAPP-Seq on advanced stage patients,we next examined other clinical scenarios in which ctDNA biomarkerscould be useful. Stage II-III NSCLC patients who undergo definitiveradiotherapy with curative intent often have surveillance CT and/orPET/CT scans that are difficult to interpret due to radiation-inducedinflammatory and fibrotic changes in the lung and surrounding tissues.These can delay diagnosis of recurrence or lead to unnecessary biopsiesand patient anxiety. To compare the results of ctDNA quantitation toroutine surveillance imaging, we analyzed pre- and post-radiotherapyplasma cfDNA in 2 patients. For patient P13, who was treated withradiotherapy alone for stage IIB NSCLC, follow-up imaging showed a largemass that was felt to represent residual disease. However, ctDNA at thesame time point was undetectable (FIG. 4 e) and the patient remaineddisease free 22 months later, supporting the ctDNA result. The secondpatient (P14) was treated with concurrent chemoradiotherapy for stageIIIB NSCLC and follow-up imaging revealed a near complete response inthe thorax (FIG. 4 f). However, the ctDNA concentration slightlyincreased compared to pre-treatment, suggesting progression of occultmicroscopic disease. Indeed, progression was detected clinically 7months later and the patient ultimately succumbed to NSCLC. These datahighlight the use of cfDNA analysis as a complementary modality toimaging studies and as a method for early diagnosis of recurrence.

We next asked whether the low detection limit of CAPP-Seq would allowmonitoring of response to treatment in early stage NSCLC. Approximately60-70% of stage I NSCLCs are curable with surgery or stereotacticablative radiotherapy (SABR). Patients P1 (FIG. 4 g) and P16 (FIG. 4 h)underwent surgery and SABR, respectively, for stage IB NSCLC. Wedetected tumor-derived cfDNA in pre-treatment plasma of P1 but not at 3or 32 months following surgery, suggesting this patient was free ofdisease and likely cured. For patient P16, the initial surveillancePET-CT scan following SABR showed a residual mass that was interpretedas representing either residual tumor or post-radiotherapy inflammation.We detected no evidence of residual disease by ctDNA, supporting thelatter, and the patient remained free of disease at last follow-up 21months after therapy. Taken together, these results demonstrate theutility of CAPP-Seq as a noninvasive clinical assay for measuring tumorburden in early and advanced stage NSCLC and for monitoring ctDNA duringdistinct types of therapy.

Noninvasive Tumor Genotyping and Cancer Screening.

Finally, we explored whether CAPP-Seq analysis of cfDNA couldpotentially be used for non-invasive tumor genotyping and cancerscreening (e.g., without prior knowledge of tumor mutations). We blindedourselves to the mutations present in each patient's tumor and applied anovel statistical method to test for the presence of cancer DNA in eachplasma sample in our cohort (FIG. 21). This method identified mutantalleles in all plasma samples containing ctDNA above fractionalabundances of 0.4%, with no false positives (FIG. 4 i). Thus, thisapproach has utility for non-invasive tumor genotyping in locallyadvanced or metastatic patients. Since ˜95% of nodules identified inpatients at high risk for developing NSCLC by low-dose CT are falsepositives, CAPP-Seq can also serve as a complementary noninvasivescreening test.

In this study, we present CAPP-Seq as a new method for ctDNAquantitation. Key features of our approach include high sensitivity andspecificity, coverage of nearly all patients with NSCLC, lack ofpatient-specific optimization, and low cost. By incorporating optimizedlibrary construction and bioinformatics methods, CAPP-Seq achieves thelowest background error rate and lowest detection limit of any NGS-basedmethod used for ctDNA analysis to date. Our approach also reduces thepotential impact of stochastic noise and biological variability (e.g.,mutations near the detection limit or subclonal tumor evolution) ontumor burden quantitation by integrating information content acrossmultiple instances and classes of somatic mutations. These featuresfacilitated the detection of minimal residual disease and the firstreport of ctDNA quantitation from stage I NSCLC tumors using deepsequencing. Although we focused on NSCLC, our method can be applied toany malignancy for which recurrent mutation data are available.

In many patients, levels of ctDNA are considerably lower than thedetection thresholds of previously described sequencing-based methods.For example, pre-treatment ctDNA concentration is <0.5% in the majorityof patients with lung and colorectal carcinomas (and likely others), and<0.1% in most early and many advanced stage patients. Following therapy,ctDNA concentrations typically drop, rendering highly sensitive methods,like CAPP-Seq, even more critical. Recently, amplicon-based deepsequencing methods were implemented to detect up to 6 recurrentlymutated genes per assay. Such approaches are limited by the number andtypes of mutations that can be simultaneously interrogated, and thereported allele detection limit of ˜2% in plasma precludes ctDNAdetection in most NSCLC patients. Several studies have reportedapplication of whole exome or genome sequencing to cfDNA for analysis ofsomatic SNVs (single nucleotide variant) and CNVs (copy number variant).The sensitivity of SNV detection with these approaches is significantlylimited by cost of sequencing, and even with 10-fold greater sequencingdepth than we used for CAPP-Seq, would be insufficient to detect ctDNAin most NSCLC patients (FIG. 5 a). Likewise, quantitation of CNVs inplasma via WGS has a reported detection limit of ˜1%, limiting thisapproach to patients with high tumor burden.

Additional gains in the detection threshold are desirable. Approaches toachieve these gains include using barcoding strategies that suppress PCRerrors resulting from library preparation, increasing the amount ofplasma used for ctDNA analysis above the average of ˜1.5 mL used in thisstudy, further improving ligation and capture efficiency during librarypreparation, and increasing the size of the selector to increase thenumber of tumor-specific mutations per patient. A second limitation isthe potential for inefficient capture of fusions, which could lead tounderestimates of tumor burden (e.g., P9). However, this bias can beanalytically addressed when other reporter types are present (e.g., P6;Table 4). Finally, while we found that CAPP-Seq could quantitate CNVs,our current selector design did not prioritize these types ofaberrations. Adding coverage for certain CNVs can be useful formonitoring various types of cancers.

In summary, targeted hybrid capture and high-throughput sequencing ofcfDNA allows for highly sensitive and non-invasive detection of ctDNA incancer patients, at low cost. CAPP-Seq can be routinely appliedclinically for accelerating the personalized detection, therapy, andmonitoring of cancer. CAPP-Seq is valuable in a variety of clinicalsettings, including the assessment of cancer DNA in alternativebiological fluids and specimens with low cancer cell content.

Patient Selection.

Between April 2010 and June 2012, patients undergoing treatment fornewly diagnosed or recurrent NSCLC were enrolled in a study approved bythe Stanford University Institutional Review Board and provided informedconsent. Enrolled patients had not received blood transfusions within 3months of blood collection. Patient characteristics are in Tables 3, 20and 21. All treatments and radiographic examinations were performed aspart of standard clinical care. Volumetric measurements of tumor burdenwere based on visible tumor on CT and calculated according to theellipsoid formula: (length/2)*(widtĥ2).

Sample Collection and Processing.

Peripheral blood from patients was collected in EDTA Vacutainer tubes(BD). Blood samples were processed within 3 hours of collection. Plasmawas separated by centrifugation at 2,500×g for 10 min, transferred tomicrocentrifuge tubes, and centrifuged at 16,000×g for 10 min to removecell debris. The cell pellet from the initial spin was used forisolation of germline genomic DNA from PBLs (peripheral bloodleukocytes) with the DNeasy Blood & Tissue Kit (Qiagen). Matched tumorDNA was isolated from FFPE specimens or from the cell pellet of pleuraleffusions. Genomic DNA was quantified by Quant-iT PicoGreen dsDNA AssayKit (Invitrogen).

Cell-Free DNA Purification and Quantification.

Cell-free DNA (cfDNA) was isolated from 1-5 mL plasma with the QIAampCirculating Nucleic Acid Kit (Qiagen). The concentration of purifiedcfDNA was determined by quantitative PCR (qPCR) using an 81 bp ampliconon chromosome 1 and a dilution series of intact male human genomic DNA(Promega) as a standard curve. Power SYBR Green was used for qPCR on aHT7900 Real Time PCR machine (Applied Biosystems), using standard PCRthermal cycling parameters.

Illumina NGS Library Construction.

Indexed Illumina NGS libraries were prepared from cfDNA and shorn tumor,germline, and cell line genomic DNA. For patient cfDNA, 7-32 ng DNA wereused for library construction without additional fragmentation. Fortumor, germline, and cell line genomic DNA, 69-1000 ng DNA was shearedprior to library construction with a Covaris S2 instrument using therecommended settings for 200 bp fragments. See Table 2 for details.

The NGS libraries were constructed using the KAPA Library PreparationKit (Kapa Biosystems) employing a DNA Polymerase possessing strong 3′-5′exonuclease (or proofreading) activity and displaying the lowestpublished error rate (e.g. highest fidelity) of all commerciallyavailable B-family DNA polymerases. The manufacturer's protocol wasmodified to incorporate with-bead enzymatic and cleanup steps usingAgencourt AMPure XP beads (Beckman-Coulter). Ligation was performed for16 hours at 16° C. using 100-fold molar excess of indexed IlluminaTruSeq adapters. Single-step size selection was performed by adding 400μL (0.8×) of PEG buffer to enrich for ligated DNA fragments. The ligatedfragments were then amplified using 500 nM Illumina backboneoligonucleotides and 4-9 PCR cycles, depending on input DNA mass.Library purity and concentration was assessed by spectrophotometer(NanoDrop 2000) and qPCR (KAPA Biosystems), respectively. Fragmentlength was determined on a 2100 Bioanalyzer using the DNA 1000 Kit(Agilent).

Design of Library for Hybrid Selection.

Hybrid selection was performed with a custom SeqCap EZ Choice Library(Roche NimbleGen). This library was designed through the NimbleDesignportal (v1.2.R1) using genome build HG19 NCBI Build 37.1/GRCh37 and withMaximum Close Matches set to 1. Input genomic regions were selectedaccording to the most frequently mutated genes and exons in NSCLC. Theseregions were identified from the COSMIC database, TCGA, and otherpublished sources. Final selector coordinates are provided in Table 1.

Hybrid Selection and High Throughput Sequencing.

NimbleGen SeqCap EZ Choice was used according to the manufacturer'sprotocol with modifications. Between 9 and 12 indexed Illumina librarieswere included in a single capture reaction. Following hybrid selection,the captured DNA fragments were amplified with 12 to 14 cycles of PCRusing 1× KAPA HiFi Hot Start Ready Mix and 2 μM Illumina backboneoligonucleotides in 4 to 6 separate 50 μL reactions. The reactions werethen pooled and processed with the QIAquick PCR Purification Kit(Qiagen). Multiplexed libraries were sequenced using 2×100 bp pared-endruns on an Illumina HiSeq 2000.

Mapping and Quality Control of NGS Data.

Paired-end reads were mapped to the hg19 reference genome with BWA 0.6.2(default parameters), and sorted/indexed with SAMtools. QC was assessedusing a custom Perl script to collect a variety of statistics, includingmapping characteristics, read quality, and selector on-target rate(e.g., number of unique reads that intersect the selector space dividedby all aligned reads), generated respectively by SAMtools flagstat,FastQC, and BEDTools coverageBed, modified to count each read at mostonce. Plots of fragment length distribution and sequence depth/coveragewere automatically generated for visual QC assessment. To mitigate theimpact of sequencing errors, analyses not involving fusions wererestricted to properly paired reads, and only bases with a Phred qualityscore ≧30 (≦0.1% probability of a sequencing error) were furtheranalyzed.

Analysis of Detection Thresholds by CAPP-Seq.

Two dilution series were performed to assess the linearity and accuracyof CAPP-Seq for quantitating tumor-derived cfDNA. In one experiment,shorn genomic DNA from a NSCLC cell line (HCC78) was spiked into cfDNAfrom a healthy individual, while in a second experiment, shorn genomicDNA from one NSCLC cell line (NCI-H3122) was spiked into shorn genomicDNA from a second NSCLC line (HCC78). A total of 32 ng DNA was used forlibrary construction. Following mapping and quality control, homozygousreporters were identified as alleles unique to each sample with at least20× sequencing depth and an allelic fraction >80%. Fourteen suchreporters were identified between HCC78 genomic DNA and plasma cfDNA(FIG. 2 g-h), whereas 24 reporters were found between NCI-H3122 andHCC78 genomic DNA (FIG. 16).

Statistical Analysis.

The NSCLC selector was validated in silico using an independent cohortof lung adenocarcinomas (FIG. 1 c). To assess statistical significance,we analyzed the same cohort using 10,000 random selectors sampled fromthe exome, each with an identical size distribution to the CAPP-SeqNSCLC selector. The performance of random selectors had a normaldistribution, and p-values were calculated accordingly. Note that allidentified somatic lesions were considered in this analysis.

To evaluate the impact of reporter number on tumor burden estimates, weperformed Monte Carlo sampling (1,000×), varying the number of reportersavailable {1, 2, . . . , max n} in two spiking experiments (FIG. 2 g-i;FIG. 13 b-d).

To assess the significance of tumor burden estimates in plasma cfDNA, wecompared patient-specific SNV frequencies to the null distribution ofselector-wide background alleles. Indels were separately analyzed usingmutation-specific background rates and Z statistics. Fusion breakpointswere considered significant when present with >0 read support due totheir ultra-low false detection rate. p-values from distinct reportertypes were integrated into a single ctDNA detection index, and this wasconsidered significant if the metric was ≦0.05 (≈FPR≦5%), the thresholdthat maximized CAPP-Seq sensitivity and specificity in ROC analyses(determined by Euclidean distance to a perfect classifier; e.g., TPR=1and FPR=0; FIG. 3, FIG. 4, Table 1, Table 4).

Related to FIG. 5, the probability P of recovering at least 2 reads of asingle mutant allele in plasma for a given depth and detection limit wasmodeled by a binomial distribution. Given P, the probability ofdetecting all identified tumor mutations in plasma (e.g., median of 4for CAPP-Seq) was modeled by a geometric distribution. Estimates in FIG.5 a are based on 250 million 100 bp reads per lane (e.g., using anIllumina HiSeq 2000 platform). Moreover, an on-target rate of 60% wasassumed for CAPP-Seq and WES (FIG. 5).

Molecular Biology Methods

Cell Lines.

The lung adenocarcinoma cell lines NCI-H3122 and HCC78 were obtainedfrom ATCC and DSMZ, respectively, and grown in RPMI 1640 withL-glutamine (Gibco) supplemented with 10% fetal bovine serum (Gembio)and 1% penicillin/streptomycin cocktail. Cells were maintained inmid-log-phase growth in a 37° C. incubator with 5% CO₂. Genomic DNA waspurified from freshly harvested cells with the DNeasy Blood & Tissue Kit(Qiagen).

Pleural Fluid Processing and Flow Cytometry, and Cell Sorting.

Cells from pleural fluid from patients P9 and P6 were harvested bycentrifugation at 300×g for 5 min at 4° C. and washed in FACS stainingbuffer (HBSS+2% heat-inactivated calf serum [HICS]). Red blood cellswere lysed with ACK Lysing Buffer (Invitrogen), and clumps were removedby passing through a 100 μm nylon filter. Filtered cells were spun downand resuspended in staining buffer. While on ice, the cell suspensionwas blocked for 20 min with 10 μg/mL rat IgG and then stained for 20 minwith APC-conjugated mouse anti-human EpCAM (BioLegend, clone 9C4),PerCP-Cy5.5-conjugated mouse anti-human CD45 (eBioscience, clone 2D1),and PerCP-eFluor710-conjugated mouse anti-human CD31 (eBioscience, cloneWM59). After staining, cells were washed and resuspended with stainingbuffer containing 1 μg/mL DAPI, analyzed, and sorted with a FACSAria IIcell sorter (BD Biosciences). Cell doublets and DAPI-positive cells wereexcluded from analysis and sorting. CD31⁻CD45⁻EpCAM⁺ cells were sortedinto staining buffer, spun down, and flash frozen in liquid nitrogen.DNA was isolated with the QIAamp DNA Micro Kit (Qiagen).

Optimization of NGS Library Preparation from Low Input cfDNA.

Protocols for Illumina library construction were compared in a step-wisemanner with the goal of (1) optimizing adapter ligation efficiency, (2)reducing the necessary number of PCR cycles following adapter ligation,(3) preserving the naturally occurring size distribution of cfDNAfragments, and (4) minimizing variability in depth of sequencingcoverage across all captured genomic regions. Initial optimization wasdone with NEBNext DNA Library Prep Reagent Set for Illumina (New EnglandBioLabs), which includes reagents for end-repair of the cfDNA fragments,A-tailing, adapter ligation, and amplification of ligated fragments withPhusion High-Fidelity PCR Master Mix. Input was 4 ng cfDNA (obtainedfrom plasma of the same healthy volunteer) for all conditions. Relativeallelic abundance in the constructed libraries was assessed by qPCR of 4genomic loci (Roche NimbleGen: NSC-0237, NSC-0247, NSC-0268, andNSC-0272) and compared by the 2^(−ΔCt) method.

Ligations were performed at 20° C. for 15 min (as per the manufacturer'sprotocol), at 16° C. for 16 hours, or with temperature cycling for 16hours as previously described. Ligation volumes were varied from thestandard (50 μL) down to 10 μL while maintaining a constantconcentration of DNA ligase, cfDNA fragments, and Illumina adapters.Subsequent optimizations incorporated ligation at 16° C. for 16 hours in50 μL reaction volumes.

Next, we compared standard SPRI bead processing procedures, in which newAMPure XP beads are added after each enzymatic reaction and DNA iseluted from the beads for the next reaction, to with-bead protocolmodifications as previously described³. We compared 2 concentrations ofIllumina adapters in the ligation reaction: 12 nM (10-fold molar excessto cfDNA fragments) and 120 nM (100-fold molar excess).

Using the optimized library preparation procedures, we next compared theNEBNext DNA Library Prep Reagent Set (with Phusion DNA Polymerase) tothe KAPA Library Preparation Kit (with KAPA HiFi DNA Polymerase). TheKAPA Library Preparation Kit with our modifications was also compared tothe NuGEN SP Ovation Ultralow Library System with automation on MondrianSP Workstation.

Evaluation of Library Preparation Modifications on CAPP-Seq Performance.

We performed CAPP-Seq on 32 ng cfDNA using standard library preparationprocedures with the NEBNext kit, or with optimized procedures usingeither the NEBNext kit or the KAPA Library Preparation Kit. In parallelwe performed CAPP-Seq on 4 ng and 128 ng cfDNA using the KAPA kit withour optimized procedures. Indexed libraries were constructed, and hybridselection was performed in multiplex. The post-capture multiplexedlibraries were amplified with Illumina backbone primers for 14 cycles ofPCR and then sequenced on a paired-end 100 bp lane of an Illumina HiSeq2000.

We also evaluated CAPP-Seq on ultralow input following whole genomeamplification (WGA). We used SeqPlex DNA Amplification Kit(Sigma-Aldrich), which employs degenerate oligonucleotide primer PCR.Briefly, 1 ng cfDNA was amplified with real-time monitoring with SYBRGreen I (Sigma-Aldrich) on a HT7900 Real Time PCR machine (AppliedBiosystems). Amplification was terminated after 17 cycles yielding 2.8μg DNA. The primer removal step yielded ˜600 ng DNA, and this entireamount was used for library preparation using the NEBNext kit withoptimized procedures as described herein.

Validation of Variants Detected by CAPP-Seq.

All structural rearrangements and a subset of tumoral SNVs detected byCAPP-Seq were independently confirmed by qPCR and/or Sanger sequencingof amplified fragments. For HCC78, a 120 bp fragment containing theSLC34A2-ROS1 breakpoint was amplified from genomic DNA using theprimers: 5′-AGACGGGAGAAAATAGCACC-3′ (SEQ ID NO: 23) and5′-ACCAAGGGTTGCAGAAATCC-3′ (SEQ ID NO: 24). For NCI-H3122, a 143 bpfragment containing the EML4-ALK breakpoint was amplified using theprimers: 5′-GAGATGGAGTTTCACTCTTGTTGC-3′ (SEQ ID NO: 25) and5′-GAACCTTTCCATCATACTTAGAAATAC-3′ (SEQ ID NO: 26). 5 ng genomic DNA wasused as template with 250 nM oligos and 1× Phusion PCR Master Mix (NEB)in 50 μL reactions. Products were resolved on 2.5% agarose gel and bandsof the expected size were removed. The amplified DNA fragments werepurified using the Qiaquick Gel Extraction Kit (Qiagen) and submittedfor Sanger sequencing (Elim Biopharm). For P9, genomic DNA breakpointswere confirmed by qPCR using the primers: 5′-TCCATGGAAGCCAGAAC-3′ (SEQID NO: 27) and 5′-ATGCTAAGATGTGTCTGTCA-3′ (SEQ ID NO: 28) for EML4-ALK;5′-CCTTAACACAGATGGCTCTTGATGC-3′ (SEQ ID NO: 29) and5′-TCCTCTTTCCACCTTGGCTTTCC-3′ (SEQ ID NO: 30) for ROS1-MKX; and5′-GGTTCAGAACTACCAATAACAAG-3′ (SEQ ID NO: 31) and5′-ACCTGATGTGTGACCTGATTGATG-3′ (SEQ ID NO: 32) for FYN-ROS1. For qPCR,10 ng of pre-amplified genomic DNA was used as template with 250 nMoligos and 1× Power SyberGreen Master Mix in 10 μL reactions performedin triplicate on a HT7900 Real Time PCR machine (Applied Biosystems).Standard PCR thermal cycling parameters were used. Amplification ofamplicons spanning all 3 breakpoints detected in P9 were confirmed intumor genomic DNA as well as plasma cfDNA, and PBL genomic DNA was usedas a negative control.

CAPP-Seq confirmed somatic tumor mutations (SNVs and rearrangements)that were detected by clinical assays as a part of standard clinicalcare (Tables 3, 20 and 21). Clinical mutation assays were performed onformalin-fixed paraffin-embedded tissues. SNVs were detected by theSNaPshot assay⁴. Rearrangements were detected by fluorescence in situhybridization (FISH) using separation probes targeting the ALK locus(Abbott) or ROS1 locus (Cytocell).

Bioinformatics and Statistical Methods

CAPP-Seq Detection Threshold Metrics.

Selector base-level background. We assessed the base-level backgrounddistribution of the NSCLC selector (FIG. 2 d) using all 40 plasma cfDNAsamples collected from NSCLC and healthy individuals analyzed in thiswork (Table 2). Specifically, for each background base in selectorpositions having ≧500× overall sequencing depth, the outlier-correctedmean across all cfDNA samples was calculated. Although we testeddedicated outlier detection methods, such as iterative Grubbs' methodand ROUT, our empirical analyses indicated that simple removal of theminimum and maximum values worked best. Importantly, to restrict ouranalysis to background bases, each patient sample was pre-filtered toremove germline, loss of heterozygosity (LOH), and/or somatic variantcalls made by VarScan 2⁶ (somatic p-value=0.01; otherwise, defaultparameters).

Significance of SNVs as reporters. To evaluate the significance oftumor-derived SNVs in plasma, we implemented a strategy that integratescfDNA fractions across all somatic SNVs, performs a position-specificbackground adjustment, and evaluates statistical significance by MonteCarlo sampling of background alleles across the selector. We note thatthis approach differs fundamentally from previous methods, wheremutations are interrogated individually. Unlike these methods, ourstrategy dampens the impact of stochastic noise and biological variables(e.g., mutations near the detection limit, or tumor evolution) on tumorburden quantitation, permitting a more robust statistical assessment. Inparticular, this allows CAPP-Seq to quantitate low levels of ctDNA withpotentially high rates of allelic drop out.

For a given plasma cfDNA sample θ, we begin by adjusting the allelicfraction f for each of n SNVs from patient P in order to minimize theinfluence of selector technical/biological background on significanceestimates. Specifically, for each allele, we perform the followingsimple operation, f*=max{0,f−(e−μ)}, where f is the raw allelic fractionin plasma cfDNA, e is the position-specific error rate for the givenallele across all cfDNA samples (see above), and μ denotes the meanselector-wide background rate (=0.006% in this study, see section B1.1and FIG. 2 d). In effect, this adjustment nudges the mean of all n SNVscloser to the global selector mean μ, mitigating the confounding impactof technical/biological background. Using Monte Carlo simulation, wecompare the adjusted mean SNV fraction F*(=(Σf*)/n) against the nulldistribution of background alleles across the selector. Specifically,for each of i iterations (=10,000 in this work), n background allelesare randomly sampled from θ, after which their fractions are adjustedusing the above formula and averaged. A SNV p-value for patient P isdetermined as the percentile of F* with respect to the null distributionof background alleles in θ. Thus, a panel of SNVs from patient P wouldbe assigned a detection p-value of 0.04 if F* ranks in the 96^(th)percentile of adjusted background alleles in θ. We note that backgroundadjustment always improved CAPP-Seq specificity in our ROC analyses.

Significance of Indels as Reporters.

We implemented an approach based on population statistics to assess thesignificance of indels separately from SNVs. For each indel in patientP, we use the Z-test to compare its fraction in a given plasma cfDNAsample θ against its fraction in every cfDNA sample in our cohort(excluding cfDNA samples from the same patient P). To increasestatistical robustness, each read strand (positive or negativeorientation) is assessed separately, yielding two Z-scores for eachindel. These are combined into a single Z-score by Stouffer's method, anunweighted approach for integrative Z statistics. Finally, if patient Phas more than 1 indel, all indel-specific Z-scores are combined byStouffer's method into a final Z statistic, which is trivially convertedto a p-value.

Significance of Fusions as Reporters.

Given the exceedingly low false positive rate associated with thedetection of the same NSCLC fusion breakpoint in independent libraries,the recovery of a tumor-derived genomic fusion in plasma cfDNA byCAPP-Seq was (arbitrarily) assigned a p-value of ˜0.

Integration of Distinct Mutation Types to Estimate Significance of TumorBurden Quantitation.

For each patient, we calculate a ctDNA detection index (akin to a falsepositive rate) based on p-value integration from his or her array ofreporters (Table 1 and Table 19). For cases where only a single reportertype is present in a patient's tumor, the corresponding p-value is used.If SNV and indel reporters are detected, and if each independently has ap-value <0.1, we combine their respective p-values by Fisher's method(Fisher, 1925), and the resulting p-value is used. Otherwise, given theprioritization of SNVs in the selector design, the SNV p-value is used.If a fusion breakpoint identified in a tumor sample (e.g., involvingROS1, ALK, or RET) is recovered in plasma cfDNA from the same patient,it trumps all other mutation types, and its p-value (˜0) is used. If afusion detected in the tumor is not found in corresponding plasma(potentially due to hybridization inefficiency; see section C4), thep-value for any remaining mutation type(s) is used. Importantly, as newpatients are processed, we cross check reporter types across the growingsample database to improve specificity (described in section B1.6,below) and identify potential red flags.

Indel/Fusion Correction for Sensitivity and Specificity Assessment.

Related to FIG. 3, after calculating a ctDNA detection index for everyset of reporters across all cfDNA samples using the methods describedherein, we applied an additional step to increase specificity. Namely,to exploit the lower technical background of indels and fusionbreakpoints as compared to SNVs, we applied an “indel/fusioncorrection”. Specifically, if indel/fusion reporters found in patientX's tumor could be uniquely detected in patient X's plasma cfDNA (e.g.,not detected in any other patient or control cfDNA sample), then thectDNA detection index corresponding to patient X was set to 1 (e.g.,ctDNA not detectable) in every unmatched cfDNA sample. In other words,patient X's reporters would not be called a false positive in anotherpatient. Although we have not yet encountered two patients with the sameindel/fusion reporter(s), if this was the case, the correction would notbe applied from one patient to the other.

To perform this correction in a blinded manner, as shown for FIG. 3(panels a and b), we identified germline SNPs in each cfDNA and PBLsample, and assigned each cfDNA sample to the tumor/normal pair withhighest SNP concordance (after un-blinding, all cfDNA samples were foundto be correctly matched to their corresponding tumor/normal pairs). Asshown in FIG. 19, this correction consistently increased CAPP-Seqspecificity. Germline SNPs were identified using VarScan 2, with ap-value threshold of 0.01, minimum sequence coverage of 100×, a minimumaverage quality score of 30 (Phred), and otherwise default parameters.

Sensitivity and Specificity Analysis.

We tested CAPP-Seq performance in a blinded fashion by masking allpatient identifying information, including disease stage, cfDNA timepoint, treatment, etc. We then tested our detection metrics describedherein for correctly calling tumor burden across the entire grid ofde-identified plasma cfDNA samples (13 patient-specific sets of somaticreporters across 40 plasma samples, or 520 pairs). To calculatesensitivity and specificity, we “un-blinded” ourselves and groupedpatient samples into cancer-positive (e.g. cancer was present in thepatient's body), cancer-negative (e.g. patient was cured), orcancer-unknown (e.g. insufficient data to determine true classification)categories. We considered every time point of patients with radiographicevidence of recurrence and all stage IV patients as cancer-positive,regardless of clinical evaluation at the time point in question. Thepost-treatment time point of patient 13 (P13; stage IIB NSCLC) wasconsidered cancer-unknown due to “No Evidence of Disease (NED)” statusat last follow-up, nearly 2 years from their treatment (FIG. 4 e).Patient 2 (P2; stage IIIB NSCLC), was classified as NED followingcomplete surgical resections, and was also considered cancer-unknown.All post-treatment stage I NSCLC patient samples were conservativelyconsidered “cancer unknown” rather than true negatives due to limitedfollow-up.

Analysis of Library Complexity

Library Complexity Estimation.

We estimated the number of haploid genome equivalents per library using330 genome equivalents per ing of input DNA (Table 2), and calculatedoverall ‘molecule recovery’ as the median depth after duplicate removaldivided by the smaller of (i) the median depth before duplicate removaland (ii) the estimated number of haploid genome equivalents. Moleculerecovery at a given sequencing depth was estimated to be 38% for cfDNA,37% for tumor DNA, and 48% for PBLs (highest DNA input mass among allsamples).

In contrast to genomic DNA, plasma cfDNA is naturally fragmented and hasa highly stereotyped size distribution related to nucleosome spacing,with a median length of ˜170 bp and very low dispersion (FIG. 2 a,Tables 3, 20 and 21). As such, we hypothesized that independent inputmolecules with identical start/end coordinates may inflate theduplication rate of cfDNA, leading to an underestimated moleculerecovery rate.

We tested this hypothesis by analyzing heterozygous germline SNPs,reasoning that DNA fragments (e.g., paired end reads) with identicalstart/end coordinates and differing by a single a priori definedgermline variant are more likely to represent independent startingmolecules than technical artifacts (e.g., PCR duplicates). HeterozygousSNPs were identified in all ninety samples (Table 2) using VarScan 2 (asdescribed herein), and filtered for variants with an allele frequencybetween 40% and 60% that are present in the Common SNPs subset of dbSNP(version 137.0). For each heterozygous common SNP, A/B, we counted allfragments with unique start/end coordinates that support A, B, or AB.Among molecules with a given A/B SNP, there is a 50% chance of getting Aand B together when randomly sampling two molecules (AB or BA), andthere is a combined 50% chance of getting either AA or BB. Since thenumber of unique start/end positions for AB (denoted N) represents atleast twice as many molecules (≧2N), and a combined ≧2N molecules can beassumed missing from unique start/end coordinates that support A or B, alower bound on total missing library complexity is determined by theformula, 3N/S, where S denotes the sum of unique start/end coordinatescovering A, B, and AB. Across SNPs in each input sample, we calculatedan average of 30% missing library complexity in cfDNA samples, and 4%and 6% missing library complexity in tumor and PBL genomic DNA,respectively (FIG. 13 a). Molecule recovery rates adjusted for estimatedloss of complexity are provided in Table 2, and indicate a mean moleculerecovery of at least 49% in cfDNA, 37% in tumor genomic DNA (mostlyFFPE) and 51% in PBL genomic DNA.

Duplication Rate.

Common deduping tools, such as SAMtools rmdup and Picard toolsMarkDuplicates (http://picard.sourceforge.net), identify and/or collapsereads based on sequence coordinates and quality, not sequencecomposition. This can result in the removal of tumor-derived reads(representing distinct molecules) that happen to share sequencecoordinates with germline reads. This is particularly problematic forcfDNA since for a large fraction of molecules there are other uniquemolecules with the same start and end (see above). To address thisissue, we developed a custom Perl script that ignores bases with lowquality (here, Phred Q<30), and collapses only those fragments (readpairs) with 100% sequence identity that also share genomic coordinates.The resulting post-duplicate reads are provided alongside correspondingnon-deduped data in Tables 2 and 4, which respectively cover sequencingstatistics and cfDNA monitoring results.

Library Complexity Measured Via PCR and Mass Input.

As a separate estimation of library complexity, for each Illumina NGSlibrary constructed from cfDNA, we calculated the fraction of expectedlibrary yield from the actual yield and the expected (ideal) yield (FIG.13 b). The actual library yield was determined from the molarity andvolume of the constructed libraries (prior to hybrid selection). Theexpected library yield was calculated from the mass of cfDNA used forlibrary preparation and the number of PCR cycles performed, with theassumption that ligation was 100% efficient and PCR was 95% efficient ateach cycle. A PCR efficiency of 95% was observed from qPCR performed onserial dilutions of Illumina TruSeq libraries (average of R²>0.999 from4 independent experiments).

CAPP-Seq Selector Design.

Most human cancers are relatively heterogeneous for somatic mutations inindividual genes. Specifically, in most human tumors, recurrent somaticalterations of single genes account for a minority of patients, and onlya minority of tumor types can be defined using a small number ofrecurrent mutations (<5-10) at predefined positions. Therefore, thedesign of the selector is vital to the CAPP-Seq method because (1) itdictates which mutations can be detected in with high probability for apatient with a given cancer, and (2) the selector size (in kb) directlyimpacts the cost and depth of sequence coverage. For example, the hybridselection libraries available in current whole exome capture kits rangefrom 51-71 Mb, providing ˜40-60 fold maximum theoretical enrichmentversus whole genome sequencing. The degree of potential enrichment isinversely proportional to the selector size such that for a ˜100 kbselector, >10,000 fold enrichment should be achievable.

We employed a six-phase design strategy to identify and prioritizegenomic regions for the CAPP-Seq NSCLC selector as detailed below. Threephases were used to incorporate known and suspected NSCLC driver genes,as well as genomic regions known to participate in clinically actionablefusions (phases 1, 5, 6), while another three phases employed analgorithmic approach to maximize both the number of patients covered andSNVs per patient (phases 2-4). The latter relied upon a metric that wetermed “Recurrence Index” (RI), defined for this example as the numberof NSCLC patients with SNVs that occur within a given kilobase of exonicsequence (e.g., No. of patients with mutations/exon length in kb). RIthus serves to measure patient-level recurrence frequency at the exonlevel, while simultaneously normalizing for gene/exon size. As a sourceof somatic mutation data uniformly genotyped across a large cohort ofpatients, in phases 2-4, we analyzed non-silent SNVs identified in TCGAwhole exome sequencing data from 178 patients in the Lung Squamous CellCarcinoma dataset (SCC) and from 229 patients in the Lung Adenocarcinoma(LUAD) datasets (TCGA query date was Mar. 13, 2012). Thresholds for eachmetric (e.g. RI and patients per exon) were selected to statisticallyenrich for known/suspected drivers in SCC and LUAD data (FIG. 7). RefSeqexon coordinates (hg19) were obtained via the UCSC Table Browser (querydate was Apr. 11, 2012).

The following algorithm was used to design the CAPP-Seq selector(parenthetical descriptions match design phases noted in FIG. 1 b).

Phase 1 (Known Drivers)

Initial seed genes were chosen based on their frequency of mutation inNSCLCs. Analysis of COSMIC (v57) identified known driver genes that arerecurrently mutated in ≧9% of NSCLC (denominator ≧500 cases). Specificexons from these genes were selected based on the pattern of SNVspreviously identified in NSCLC. The seed list also included single exonsfrom genes with recurrent mutations that occurred at low frequency buthad strong evidence for being driver mutations, such as BRAF exon 15,which harbors V600E mutations in <2% of NSCLC.

Phase 2 (Max. Coverage)

For each exon with SNVs covering ≧5 patients in LUAD and SCC, weselected the exon with highest RI that identified at least 1 new patientwhen compared to the prior phase. Among exons with equally high RI, weadded the exon with minimum overlap among patients already captured bythe selector. This was repeated until no further exons met thesecriteria.

Phase 3 (RI≧30)

For each remaining exon with an RI≧30 and with SNVs covering ≧3 patientsin LUAD and SCC, we identified the exon that would result in the largestreduction in patients with only 1 SNV. To break ties among equally bestexons, the exon with highest RI was chosen. This was repeated until noadditional exons satisfied these criteria.

Phase 4 (RI≧20)

Same procedure as phase 3, but using RI≧20.

Phase 5 (Predicted Drivers)

We included all exons from additional genes previously predicted toharbor driver mutations in NSCLC.

Phase 6 (Add Fusions)

For recurrent rearrangements in NSCLC involving the receptor tyrosinekinases ALK, ROS1, and RET, the introns most frequently implicated inthe fusion event and the flanking exons were included.

All exons included in the selector, along with their corresponding HUGOgene symbols and genomic coordinates, as well as patient statistics forNSCLC and a variety of other cancers, are provided in Table 1, organizedby selector design phase.

CAPP-Seq Computational Pipeline

Mutation Discovery: SNVs/Indels.

For detection of somatic SNV and insertion/deletion events, we employedVarScan 2 (somatic p-value=0.01, minimum variant frequency=5%, strandfilter=true, and otherwise default parameters). Somatic variant calls(SNV or indel) present at less than 0.5% mutant allelic frequency in thepaired normal sample (PBLs), but in a position with at least 1000×overall depth in PBLs and 100× depth in the tumor, and with at least 1×read depth on each strand, were retained (Tables 3, 20 and 21). Whilethe selector was designed to predominantly capture exons, in practice,it also captures limited sequence content flanking each targeted region.For instance, this phenomenon is the basis for the (thus far) uniformlysuccessful recovery by CAPP-Seq of fusion partners (which are notincluded within the selector) for kinase genes such as ALK and ROS1recurrently rearranged in NSCLC. As such, we also considered variantcalls detected within 500 bps of defined selector coordinates. Thesecalls were eliminated if present in non-coding repeat regions, sincerepeats may confound mapping accuracy. Repeat sequence coordinates wereobtained using the RepeatMasker track in the UCSC table browser (hg19).Given a low, but measurable cross-contamination rate of ˜0.06% inmultiplexed cfDNA samples, (FIG. 14) we also excluded any SNVs found asgermline SNPs in samples from the same lane. Additionally, we excludedSNVs in the top 99.9^(th) percentile of global selector background(>0.27% sample-wide background rate; see FIG. 2 d and section B1.1above). Finally, we excluded any SNVs not present at a depth of at least500× in at least 1 cfDNA sample. Variant annotation was automaticallydownloaded from the SeattleSeq Annotation 137 web server. Completedetails for all identified SNVs and indels are provided in Tables 3, 20and 21. Of note, all depth thresholds refer to pre-duplication removalreads.

Mutation Discovery: Fusions.

For practical and robust de novo enumeration of genomic fusion eventsand breakpoints from paired-end next-generation sequencing data, wedeveloped a novel heuristic approach, termed FACTERA (FACileTranslocation Enumeration and Recovery Algorithm). FACTERA has minimalexternal dependencies, works directly on a preexisting .bam alignmentfile, and produces easily interpretable output. Major steps of thealgorithm are summarized below, and are complemented by a graphicalschematic to illustrate key elements of the breakpoint identificationprocess (FIG. 8). FACTERA is coded in Perl and freely available uponrequest.

As input, FACTERA requires a .bam alignment file of paired-end readsproduced by BWA, exon coordinates in .bed format (e.g., hg19 RefSeqcoordinates), and a 0.2 bit reference genome to enable fast sequenceretrieval (e.g., hg19). In addition, the analysis can be optionallyrestricted to reads that overlap particular genomic regions (.bed file),such as the CAPP-Seq selector used in this work.

FACTERA processes the input in three sequential phases: identificationof discordant reads, detection of breakpoints at base pair-resolution,and in silico validation of candidate fusions. Each phase is describedin detail below.

Identification of Discordant Reads.

To iteratively reduce the sequence space for gene fusion identification,FACTERA, like other algorithms (e.g. BreakDancer), identifies andclassifies discordant read pairs. Such reads indicate a nearby fusionevent since they either map to different chromosomes or are separated byan unexpectedly large insert size (e.g. total fragment length), asdetermined by the BWA mapping algorithm. The bitwise flag accompanyingeach aligned read encodes a variety of mapping characteristics (e.g.,improperly paired, unmapped, wrong orientation, etc.) and is leveragedto rapidly filter the input for discordant pairs. The closest exon ofeach discordant read is subsequently identified, and used to clusterdiscordant pairs into distinct gene-gene groups, yielding a list ofgenomic regions R adjacent to candidate fusion sites. For each membergene of a discordant gene pair, the genomic region R_(i) is defined bytaking the minimum of all 3′ exon/read coordinates in the cluster, andthe maximum of all 5′ exon/read coordinates in the cluster. Theseregions are used to prioritize the search for breakpoints in the nextphase (FIG. 8 a).

Detection of Breakpoints at Base Pair-Resolution.

Discordant read pairs may be introduced by NGS library preparationand/or sequencing artifacts (e.g., jumping PCR). However, they are alsolikely to flank the breakpoints of bona fide fusion events. As such, alldiscordant gene pairs identified in the preceding phase are ranked indecreasing order of discordant read depth (duplicate fragments areeliminated to correct for possible PCR bias), and genomic regions with adepth of at least 2× (by default) are further evaluated for potentialbreakpoints. Within each region, FACTERA analyzes all properly pairedreads in which one of the two reads is “soft-clipped,” or truncated (seeFIG. 8 a). Soft-clipped reads allow for precise breakpointdetermination, and are easily identified by parsing the CIGAR stringassociated with each mapped read, which compactly specifies thealignment operation used on each base (e.g. My=y contiguous bases weremapped, Sx=x bases were skipped). To simplify this step, onlysoft-clipped reads with the following two patterns are considered, SxMyand MySx, and the number of skipped bases x is required to be at least16 (≦1 in 4.3B by random chance) to reduce the impact of non-specificsequence alignments.

To validate potential genomic breakpoints, defined as the edges ofsoft-clipped reads, FACTERA executes the following routine, depicted inFIG. 8. For each discordant gene pair (e.g. genes w and v in FIG. 8 a),all candidate breakpoints are tabulated, and the support (e.g. readfrequency) for each is determined Breakpoints supported by less than 2reads (by default) are excluded from further analysis. Starting with thetwo breakpoints with highest support, FACTERA selects a representativesoft-clipped read for each breakpoint, such that the length of theclipped sequence is closest to half of the read length (FIG. 8 b). Ifthe mapped region of one read matches the soft-clipped region of theother, FACTERA records a putative fusion event. To assess inter-readconcordance (e.g. see reads 1 and 2 in FIG. 8 c), FACTERA employs thefollowing algorithm. The mapped region of read 1 is parsed into allpossible subsequences of length k (e.g., k-mers) using a sliding window(k=10, by default). Each k-mer, along with its lowest sequence index inread 1, is stored in a hash table data structure, allowing k-mermembership to be assessed in constant time (FIG. 8 c, left panel).Subsequently, the soft clipped sequence of read 2 is parsed intosubsequences of length k, and the hash table is interrogated formatching k-mers (FIG. 8 c, right panel). If a minimum matching thresholdis achieved (=0.5×the minimum length of the two compared subsequences),then the two reads are considered concordant. FACTERA will process atmost 1000 (by default) putative breakpoint pairs for each discordantgene pair. Moreover, for each gene pair, FACTERA will only compare readswhose orientations are compatible with valid fusions. Such reads havesoft-clipped sequences facing opposite directions (FIG. 8 d, top panel).When this condition is not satisfied, FACTERA uses the reversecomplement of read 1 for k-mer analysis (FIG. 8 d, bottom panel).

In some instances, genomic subsequences flanking the true breakpoint maybe nearly or completely identical, causing the aligned portions ofsoft-clipped reads to overlap. Unfortunately, this prevents anunambiguous determination of the breakpoint. As such, FACTERAincorporates a simple algorithm to arbitrarily adjust the breakpoint inone read (e.g., read 2) to match the other (e.g., read 1). Dependingupon read orientation, there are two ways this can occur, both of whichare illustrated in FIG. 8 e. For each read, FACTERA calculates thedistance between the breakpoint and the read coordinate corresponding tothe first k-mer match between reads. For example, as anecdotallyillustrated in FIG. 8 e, x is defined as the distance between thebreakpoint coordinate of read 1 and the index of the first matchingk-mer, j, whereas y denotes the corresponding distance for read 2. Theoffset is estimated as the difference in distances (x, y) between thetwo reads (see FIG. 8 e).

In Silico Validation of Candidate Fusions.

To confirm each candidate breakpoint in silico, FACTERA performs a localrealignment of reads against a template fusion sequence (±500 bp aroundthe putative breakpoint) extracted from the 0.2 bit reference genome.BLAST is currently employed for this purpose, although BLAT or otherfast aligners could be substituted. A BLAST database is constructed bycollecting all reads that map to each candidate fusion sequence,including discordant reads and soft-clipped reads, as well as allunmapped reads in the original input .bam file. All reads that map to agiven fusion candidate with at least 95% identity and a minimum lengthof 90% of the input read length (by default) are retained, and readsthat span or flank the breakpoint are counted. As a final step, outputredundancies are minimized by removing fusion sequences within a 20 bpinterval of any fusion sequence with greater read support and with thesame sequence orientation (to avoid removing reciprocal fusions).

FACTERA produces a simple output text file, which includes for eachfusion sequence, the gene pair, the chromosomal sequence coordinates ofthe breakpoint, the fusion orientation (e.g., forward-forward orforward-reverse), the genomic sequences within 50 bp of the breakpoint,and depth statistics for reads spanning and flanking the breakpoint.Fusions identified in patients analyzed in this work are provided inTables 3, 20 and 21.

Experimental Validation of FACTERA.

To experimentally evaluate the performance of FACTERA, we generated NGSdata from two NSCLC cell lines, HCC78 (21.5M×100 bp paired-end reads)and NCI-H3122 (19.4M×100 bp paired-end reads), each of which has a knownrearrangement (ROS1 and ALK, respectively) with a breakpoint that has,to the best of our knowledge, not been previously published. FACTERAreadily revealed evidence for a reciprocal SLC34A2-ROS1 translocation inthe former and an EML4-ALK fusion in the latter. Precise breakpointspredicted by FACTERA were experimentally validated by PCR amplificationand Sanger sequencing (FIG. 9; see also Validation of Variants Detectedby CAPP-Seq). Importantly, FACTERA completed each run in practical time(˜90 sec), using only a single thread on a hexa-core 3.4 GHz Intel XeonE5690 chip. These initial results illustrate the utility of FACTERA aspart of the CAPP-Seq analysis pipeline.

Templated Fusion Discovery.

We implemented a user-directed option to “hunt” for fusions withinexpected candidate genes. A fusion could be missed by FACTERA if thefusion detection criteria employed by FACTERA are incompletelysatisfied—such as if discordant reads, but not soft-clipped reads, areidentified—and will most likely occur when fusion allele frequency inthe tumor is extremely low. As input, the method is supplied withcandidate fusion gene sequences as “baits”. All unmapped andsoft-clipped reads in the input .bam file are subsequently aligned tothese templates (using blastn) to identify reads that have sufficientsimilarity to both (for each read, 95% identity, e-value<1.0e-5, and atleast 30% of the read length must map to the template, by default). Suchreads are output as a list to the user for manual analysis.

We tested this simple approach on a low purity tumor sample found toharbor an ALK fusion by FISH, but not FACTERA (e.g., case P9). Usingtemplates for ALK and its common fusion partner, ELM4, we identified 4reads that mapped to both, in a region with an overall depth of ˜1900×.The estimated allele frequency of 0.21% is strikingly similar to the0.22% tumor purity measured by FACS (FIG. 17), confirming the utility ofthe templated fusion discovery method. We subsequently FACS-depletedCD45+ immune populations and re-sequenced this patient's tumor. In theenriched tumor sample, FACTERA identified the EML4-ALK fusion, alongwith two novel ROS1 fusions (FIG. 4 b, Tables 3, 20 and 21).

Mutation Recovery:SNVs/Indels.

Using a custom Perl script, previously identified reporter alleles wereintersected with a SAMtools mpileup file generated for each plasma cfDNAsample, and the number and frequency of supporting reads was calculatedfor each reporter allele. Only reporters in properly paired reads atpositions with at least 500× overall depth (pre-duplication removal)were considered (Table 4).

Mutation Recovery: Fusions.

For enumeration of fusion frequency in sequenced plasma DNA, FACTERAexecutes the last step of the discovery phase (e.g., in silicovalidation of candidate fusions, above) using the set of previouslyidentified fusion templates. The fusion allele frequency is calculatedas α/β, where α is the number of breakpoint-spanning reads, and β is themean overall depth within a genomic region ±5 bps around the breakpoint.Regarding the NSCLC selector described in this work, the lattercalculation was always performed on the single gene contained in theNSCLC selector library. If both fusion genes are targeted within aselector library, overall depth is estimated by taking the mean depthcalculated for both genes.

Notably, in some cases we observed lower fusion allele frequencies thanwould be expected for heterozygous alleles (e.g., see cell line fusionsin Tables 3, 20 and 21). This was seen in cell lines, in an empiricalspiking experiment, and in one patient's tumor and plasma samples (e.g.,P6), and could potentially result from inefficient “pull-down” offusions whose partners are not represented in the selector. Regardless,fusions are useful reporters—they possess virtually no background signaland show linear behavior over defined concentrations in a spikingexperiment (FIG. 16 d). Moreover, allelic frequencies in plasma areeasily adjusted for such inefficiencies by dividing the measuredfrequency in plasma by the corresponding frequency in the tumor. Incases where sequenced tumor tissue is impure, tumor content can beestimated using the frequencies of SNVs (or indels) as a referenceframe, allowing the fusion fraction to be normalized accordingly (Table4).

Screening Plasma cfDNA without Knowledge of Tumor DNA.

We devised the following statistical algorithm as an initial step towardnon-invasive tumor genotyping and cancer screening with CAPP-Seq. Themethod identifies candidate SNVs using iterative models of (i)background noise in paired germline DNA (in this work, PBLs), (ii)base-pair resolution background frequencies in plasma cfDNA across theselector, and (iii) sequencing error in cfDNA. Examples are provided inFIG. 21. The algorithm works in four main steps, detailed below.

As input, the algorithm takes allele frequencies from a single plasmacfDNA sample and analyzes high quality background alleles, defined in afirst step for each genomic position as the non-dominant base withhighest fractional abundance. Only alleles with depth of at least 500×and strand bias <90% (conservative, by default) are analyzed. Forconsistency with variant calling, we allowed the screening approach tointerrogate selector regions within 500 bp of defined coordinates,expanding the effective sequence space from ˜125 kb to ˜600 kb.

Second, the binomial distribution is used to test whether a given inputcfDNA allele is significantly different from the corresponding pairedgermline allele (FIG. 21 a-b). Here the probability of success is takento be the frequency of the background allele in PBLs, and the number oftrials is the allele's corresponding depth in plasma cfDNA. To avoidcontributions from alleles in rare circulating tumor cells that mightcontaminate PBLs, input alleles with a fractional abundance greater than0.5% in paired PBLs (by default) or a Bonferroni-adjusted binomialprobability greater than 2.08×10⁻⁸ are not further considered (alpha of0.05/[˜600 kb*4 alleles per position]).

Third, a database of cfDNA background allele frequencies is assembled.Here, we used samples analyzed in the present study (e.g., pre-treatmentNSCLC samples and 1 sample from a healthy volunteer), except the inputsample is left out to avoid bias. Based on the assumption that allbackground allele fractions follow a normal distribution, a Z-test isemployed to test whether a given input allele differs significantly fromtypical cfDNA background at the same position (FIG. 21 a-b). All alleleswithin the selector are evaluated, and those with an average backgroundfrequency of 5% or greater (by default) or a Bonferroni-adjustedsingle-tailed Z-score <5.6 are not further considered (alpha of 0.05,adjusted as above).

Finally, candidate alleles are tested for remaining possible sequencingerrors. This step leverages the observation that non-tumor variants(e.g., “errors”) in plasma cfDNA tend to have a higher duplication ratethan bona fide variants detectable in the patient's tumor (data notshown). As such, the number of supporting reads is compared for eachinput allele between nondeduped (all fragments meeting QC critiera) anddeduped data (only unique fragments meeting QC criteria). An outlieranalysis is then used to distinguish candidate tumor-derived SNVs fromremaining background noise (FIG. 21 a-c). Specifically, to revealoutlier tendency in the data, the square root of the robust distance Rd(Mahalanobis distance) is compared against the square root of thequantiles of a chi-squared distribution Cs. This transformation revealsnatural separation between true SNVs and false positives in cancerpatients (FIG. 21 a, c), and notably, reveals an absence of outlierstructure in patient samples lacking tumor-derived SNVs (FIG. 21 b, c).To automatically call SNVs without prior knowledge, the screeningapproach iterates through data points by decreasing Rb and recalculatingthe Pearson's correlation coefficient Rho between Rd and Cs for points 1to i, where Rd_(i) is the current maximum Rd. The algorithm iterativelyreports outliers (e.g., candidate SNVs) until it terminates whenRho≧0.85

Example 2 Designing a Personalized Selector Set

In certain circumstances, monitoring tumor burden in a patient known tohave cancer is likely to be impractical using an ‘off-the-shelf’strategy applying knowledge from a cohort of patients with the sametumor type, to selectively capture genomic regions that are recurrentlymutated in that tumor type using CAPP-Seq. These situations include, butare not limited to, cases where (1) the tumor is of an unknown primaryhistology (e.g., CUP); (2) the histology is known, but is too rare tohave a sufficient number of patients with that tumor type previouslyprofiled to define the average patient's tumor somatic genetic landscape(e.g., soft tissue sarcoma subtyped); (3) the histology is known but theaverage/median number of recurrent somatic lesions in that tumor typeare too low to achieve desired sensitivity levels (e.g., pediatrictumors, etc.); or (4) the histology is known and the average/mediannumber of recurrent somatic lesions is reasonable, but the averageburden of tumor volume is so small that additional sensitivity can beachieved using more mutations per tumor (e.g., early stages of malignantmelanoma). In such cases, a personalized strategy for monitoring tumorburden is likely to overcome these hurdles for disease monitoring.

Here, tumor(s) from a patient known to have cancer are genotyped byprofiling the tumor genome, exome, or targeted region expected to beenriched for somatic aberrations. The genotype of the cancer may becompared to a genotype of the germline of the same patient. Theresulting lesions are then catalogued and used to build a custom,personalized selector comprising a set of biotinylated oligonucleotidesfor selective hybrid affinity capture of corresponding circulating tumorDNA (ctDNA) molecules. Cell-free DNA circulating in blood or body fluidsand harboring such ctDNA molecules would be isolated, and used to buildshotgun genomic libraries that include ligation of molecular tags(‘barcodes’) that distinguish such sequences from others, allowing forsuppression of spurious errors introduced during the amplification ofcfDNA using thermostable DNA polymerases as part of polymerase chainreaction. The personalized selector would then be applied for capture ofthe fragments of interest, sequenced and analyzed in the same manner asthe ‘off-the-shelf’ CAPP-Seq workflow, allowing the tracking andquantitation of those mutations originally discovered in the primarytumor within the corresponding cfDNA. As an alternative to affinitybased hybrid capture of ctDNA/cfDNA, amplicons specific to thecorresponding region could be interrogated by PCR, with such fragmentsselectively indexed using molecular barcodes that similarly allowdistinction of sequencing errors introduced during PCR.

Example 3 Use of a Selector Set to Diagnose a Cancer

A plasma sample is obtained from a female subject with an abnormal lumpin her breast. Cell-free DNA (cfDNA) is extracted from the plasmasample. An end repair reaction is performed on the cfDNA by mixing thecomponents in a sterile microfuge tube (or other suitable sterilecontainer) as follows:

Component Volume (μL) cfDNA 1-75 Phosphorylation Reaction Buffer (10X)10 T4 DNA polymerase 5 T4 Polynucleotide kinase 5 dNTPs 4 DNA PolymeraseI, Large (Klenow) 1 Sterile H₂O -bring total volume up to 100 μL

The end repair reaction mixture is incubated in a thermal cycler for 30minutes at 20° C.

Clean-up of the end repaired cfDNA is performed by adding 160 μL (1.6×)of resuspended AMPure XP beads to the end repair reaction mixture. TheAMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction isincubated for 5 minutes at room temperature. The reaction is placed on amagnetic stand to separate the beads from the supernatant. After thesolution is clear (approximately 5 minutes), the supernatant is removedand discarded. The beads are washed twice by adding 200 μL of 80%freshly prepared ethanol to the reaction while in the magnetic stand.For each wash, the ethanol solution is added at room temperature for 30seconds. The supernatant is removed and discarded. The beads are airdried for 10 minutes while the reaction is on the magnetic stand. cfDNAis eluted from the beads by adding 40 μL of sterile water and vortexingor pipetting the water up and down. The reaction is placed back on themagnetic stand. Once the solution is clear, 32 μL of the supernatant istransferred to a fresh, sterile container (e.g., microfuge tube).

dA-tailing of the end repaired cfDNA is performed by mixing thefollowing components in the sterile microfuge tube as follows:

Component Volume (μL) End repaired cfDNA 32 NEBuffer 2 (10X) 5Deoxyadenosine 5′-Triphosphate 10 Klenow Fragment (3′→5′ exo-) 3

The dA-tailing reaction is incubated in a thermal cycle for 30 minutesat 37° C.

Clean-up of the dA-tailed cfDNA is performed by adding 90 μL (1.8×) ofresuspended AMPure XP beads to the dA-tailing reaction mixture. TheAMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction isincubated for 5 minutes at room temperature. The reaction is placed on amagnetic stand to separate the beads from the supernatant. After thesolution is clear (approximately 5 minutes), the supernatant is removedand discarded. The beads are washed twice by adding 200 μL of 80%freshly prepared ethanol to the reaction while in the magnetic stand.For each wash, the ethanol solution is added at room temperature for 30seconds. The supernatant is removed and discarded. The beads are airdried for 10 minutes while the reaction is on the magnetic stand. cfDNAis eluted from the beads by adding 15 μL of sterile water and vortexingor pipetting the water up and down. The reaction is placed back on themagnetic stand. Once the solution is clear, 10 μL of the supernatant istransferred to a fresh, sterile container (e.g., microfuge tube).

Adaptor ligation of the dA-tailed cfDNA is performed by mixing thefollowing components in the sterile microfuge tube as follows:

Component Volume (μL) dA-tailed cfDNA 10 Quick Ligation Reaction Buffer(2X) 25 Illumina Adaptor 10 Quick T4 DNA Ligase 5

The adaptor ligation reaction is incubated at 16° C. for 16 hours. Theadaptor ligation reaction is terminated by adding 3 μL of USER™ enzymemix by pipetting up and down and incubation at 37° C.

Clean-up of the adaptor-ligated cfDNA is performed by adding 90 μL(1.8×) of resuspended AMPure XP beads to the adaptor ligation reactionmixture. The AMPure beads are mixed into the solution on a vortex mixeror by pipetting up and down (e.g., 10 times or more). The reaction isincubated for 5 minutes at room temperature. The reaction is placed on amagnetic stand to separate the beads from the supernatant. After thesolution is clear (approximately 5 minutes), the supernatant is removedand discarded. The beads are washed twice by adding 200 μL of 80%freshly prepared ethanol to the reaction while in the magnetic stand.For each wash, the ethanol solution is added at room temperature for 30seconds. The supernatant is removed and discarded. The beads are airdried for 10 minutes while the reaction is on the magnetic stand. cfDNAis eluted from the beads by adding 105 μL of sterile water and vortexingor pipetting the water up and down. The reaction is placed back on themagnetic stand. Once the solution is clear, 100 μL of the supernatant istransferred to a fresh, sterile container (e.g., microfuge tube).

Universal PCR amplification is performed on the adaptor-ligated cfDNAusing primers targeting the adaptors. The PCR amplification is conductedusing 14 amplification cycles. Selector set probes are used toselectively capture a subset of the amplified products of the adaptorligated cfDNA. Sequencing reactions are performed on the capturedamplified products. The captured amplified cfDNA is sequenced on apaired-end 100 bp lane of an Illumina HiSeq 2000.

The sequencing information is analyzed by detecting mutations in one ormore genomic regions based on a selector set. The selector set containsinformation pertaining to mutations occurring in one or more genomicregions, wherein the mutations are present in at least about 70% of apopulation of subjects suffering from a breast cancer. In order todetermine the statistical significance of the mutations detected in thesample, p-values for the different classes of mutations are calculated.A ctDNA detection index is used to evaluate the statistical significanceof detecting two or more classes of mutations.

A report of the mutations detected in the sample and the statisticalsignificance of the detection of the mutations is provided to aphysician. Based on the detection of at least three mutations in threegenomic regions, the physician diagnoses a breast cancer in the subject.

Example 4 Use of a Selector Set to Determine a Status or Outcome of aCancer

Cell-free DNA (cfDNA) is purified from a sample from a subject diagnosedwith a prostate cancer. An end repair reaction is performed on the cfDNAby mixing the components in a sterile microfuge tube (or other suitablesterile container) as follows:

Component Volume (μL) 1-5 μg cfDNA 1-85 10X End Repair Buffer 10 EndRepair Enzyme Mix  5 Sterile H₂O -bring total volume up to 100 μL

The end repair reaction mixture is incubated in a thermal cycler for 30minutes at 20° C.

Clean-up of the end repaired cfDNA is performed by adding 160 μL (1.6×)of resuspended AMPure XP beads to the end repair reaction mixture. TheAMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 15 minutes oruntil the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 15 minutes while the reaction is on themagnetic stand. cfDNA is eluted from the beads by resuspending the beadsthoroughly in 32.5 μL of elution buffer and incubating at roomtemperature for 2 minutes. The reaction is placed back on the magneticstand at room temperature for 15 minutes or until the solution is clear.30 μL of the supernatant is transferred to a fresh, sterile container(e.g., microfuge tube).

dA-tailing of the end repaired cfDNA is performed by mixing thefollowing components in the sterile microfuge tube as follows:

Component Volume (μL) End repaired cfDNA 30 10X A-tailing buffer 5A-tailing enzyme 3 Sterile water 12

The dA-tailing reaction is incubated in a thermal cycle for 30 minutesat 30° C.

Clean-up of the dA-tailed cfDNA is performed by adding 90 μL (1.8×) ofresuspended AMPure XP beads to the dA-tailing reaction mixture. TheAMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 15 minutes oruntil the reaction is clear. After the solution is clear (approximately5 minutes), the supernatant is removed and discarded. The beads arewashed twice by adding 200 μL of 80% freshly prepared ethanol to thereaction while in the magnetic stand. For each wash, the ethanolsolution is added at room temperature for 30 seconds. The supernatant isremoved and discarded. The beads are air dried for 15 minutes while thereaction is on the magnetic stand. cfDNA is eluted from the beads byresuspending the beads thoroughly in 32.5 μL of elution buffer andincubating at room temperature for 2 minutes. The reaction is placedback on the magnetic stand for 15 minutes at room temperature or untilthe solution is clear. 30 μL of the supernatant is transferred to afresh, sterile container (e.g., microfuge tube).

Adaptor ligation of the dA-tailed cfDNA is performed by mixing thefollowing components in the sterile microfuge tube as follows:

Volume Component (μL) dA-tailed cfDNA 30 5X Ligation Buffer 10 IlluminaAdaptor 5 DNA Ligase 5

The adaptor ligation reaction is incubated at 16° C. for 16 hours.

Clean-up of the adaptor-ligated cfDNA is performed by adding 50 μL ofresuspended AMPure XP beads to the adaptor ligation reaction mixture.The AMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 15 minutes oruntil the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 15 minutes while the reaction is on themagnetic stand. The beads are resuspended in 52.5 μL of elution buffer.The reaction is placed back on the magnetic stand and incubated at roomtemperature for 15 minutes or until the solution is clear. 50 μL of thesupernatant is transferred to a fresh, sterile container (e.g.,microfuge tube).

A second clean-up of the adaptor-ligated cfDNA is performed by adding 50μL of resuspended AMPure XP beads to the adaptor ligation reactionmixture. The AMPure beads are mixed into the solution on a vortex mixeror by pipetting up and down (e.g., 10 times or more). The reaction isplaced on a magnetic stand and incubated at room temperature for 15minutes or until the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 15 minutes while the reaction is on themagnetic stand. The beads are resuspended in 32.5 μL of elution bufferand incubated at room temperature for 2 minutes. The reaction is placedback on the magnetic stand and incubated at room temperature for 15minutes or until the solution is clear. 30 μL of the supernatant istransferred to a fresh, sterile container (e.g., microfuge tube).

Universal PCR amplification is performed on the adaptor-ligated cfDNAusing primers targeting the adaptors. The PCR amplification is conductedusing 16 amplification cycles. Selector set probes are used toselectively capture a subset of the amplified adaptor ligated cfDNA. Theamplified cfDNA is sequenced on a paired-end 100 bp lane of an IlluminaHiSeq 2000.

The sequencing information is analyzed by detecting mutations in one ormore genomic regions based on a selector set. The selector set containsinformation pertaining to mutations occurring in one or more genomicregions, wherein the mutations are present in at least about 70% of apopulation of subjects suffering from a breast cancer. A quantity ofcirculating tumor-DNA (ctDNA) is determined based on the sequencingreads.

A report comprising the quantity of the ctDNA is provided to aphysician. Based on the quantity of the ctDNA, the physician provides aprognosis of the prostate cancer in the subject.

Example 5 Use of a Selector Set to Determine a Therapeutic Regimen forthe Treatment of a Cancer

Cell-free DNA (cfDNA) is purified from a sample from a subject diagnosedwith a thyroid cancer. An end repair reaction is performed on the cfDNAby mixing the components in a sterile microfuge tube (or other suitablesterile container) as follows:

Component Volume (μL) 1-5 μg cfDNA 1-85 10X End Repair Buffer 10 EndRepair Enzyme Mix  5 Sterile H₂O -bring total volume up to 100 μL

The end repair reaction mixture is incubated in a thermal cycler for 30minutes at 20° C.

Clean-up of the end repaired cfDNA is performed by adding 160 μL (1.6×)of resuspended AMPure XP beads to the end repair reaction mixture. TheAMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 15 minutes oruntil the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 15 minutes while the reaction is on themagnetic stand. cfDNA is eluted from the beads by resuspending the beadsthoroughly in 32.5 μL of elution buffer and incubating at roomtemperature for 2 minutes. The reaction is placed back on the magneticstand at room temperature for 15 minutes or until the solution is clear.30 μL of the supernatant is transferred to a fresh, sterile container(e.g., microfuge tube).

dA-tailing of the end repaired cfDNA is performed by mixing thefollowing components in the sterile microfuge tube as follows:

Component Volume (μL) End repaired cfDNA 30 10X A-tailing buffer 5A-tailing enzyme 3 Sterile water 12

The dA-tailing reaction is incubated in a thermal cycle for 30 minutesat 30° C.

Clean-up of the dA-tailed cfDNA is performed by adding 90 μL (1.8×) ofresuspended AMPure XP beads to the dA-tailing reaction mixture. TheAMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 15 minutes oruntil the reaction is clear. After the solution is clear (approximately5 minutes), the supernatant is removed and discarded. The beads arewashed twice by adding 200 μL of 80% freshly prepared ethanol to thereaction while in the magnetic stand. For each wash, the ethanolsolution is added at room temperature for 30 seconds. The supernatant isremoved and discarded. The beads are air dried for 15 minutes while thereaction is on the magnetic stand. cfDNA is eluted from the beads byresuspending the beads thoroughly in 32.5 μL of elution buffer andincubating at room temperature for 2 minutes. The reaction is placedback on the magnetic stand for 15 minutes at room temperature or untilthe solution is clear. 30 μL of the supernatant is transferred to afresh, sterile container (e.g., microfuge tube).

Adaptor ligation of the dA-tailed cfDNA is performed by mixing thefollowing components in the sterile microfuge tube as follows:

Component Volume (μL) dA-tailed cfDNA 30 5X Ligation Buffer 10 Adaptor 5DNA Ligase 5

The adaptor ligation reaction is incubated at 16° C. for 16 hours. Theconcentration of the adaptor is increased through the duration of theincubation. The adaptor is a Y-shaped adaptor. The 5′ strand of thesplit portion of the Y-shaped contains a molecular barcode and a sampleindex. The double stranded portion of the Y-shaped adaptor contains auniversal sequence. The universal sequence is used for PCR enrichmentand sequencing.

Clean-up of the adaptor-ligated cfDNA is performed by adding 50 μL ofresuspended AMPure XP beads to the adaptor ligation reaction mixture.The AMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 5 minutes oruntil the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 15 minutes while the reaction is on themagnetic stand. The beads are resuspended in 52.5 μL of elution buffer.The reaction is placed back on the magnetic stand and incubated at roomtemperature for 5 minutes or until the solution is clear. 50 μL of thesupernatant is transferred to a fresh, sterile container (e.g.,microfuge tube).

A second clean-up of the adaptor-ligated cfDNA is performed by adding 50μL of resuspended AMPure XP beads to the adaptor ligation reactionmixture. The AMPure beads are mixed into the solution on a vortex mixeror by pipetting up and down (e.g., 10 times or more). The reaction isplaced on a magnetic stand and incubated at room temperature for 5minutes or until the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 10 minutes while the reaction is on themagnetic stand. The beads are resuspended in 105 μL of elution bufferand incubated at room temperature for 2 minutes. The reaction is placedback on the magnetic stand and incubated at room temperature until thesolution is clear. 100 μL of the supernatant is transferred to a fresh,sterile container (e.g., microfuge tube).

Bead based size selection of the adaptor ligated cfDNA is performed byadding 80 μL of AMPure XP beads to the adaptor ligated cfDNA. Thereaction is mixed by vortexing the reaction or pipetting the solution upand down at least 10 times. The reaction is incubated at roomtemperature for 5 minutes. The reaction is placed on a magnetic standfor 5 minutes or until the solution is clear. Once the solution isclear, the supernatant is transferred to a new tube. 20 μL of AMPure XPbeads are added to the supernatant (vortex or pipet up and down to mix)and incubated at room temperature for 5 minutes. The reaction is placedon the magnetic stand for 5 minutes or until the solution is clear. Oncethe solution is clear, the supernatant is removed and discarded. Whileon the magnetic stand, the beads are washed twice using 200 μL offreshly prepared 80% ethanol. The ethanol washes are incubated at roomtemperature for 30 seconds and removed and discarded. The beads are airdried at room temperature for 10 minutes. cfDNA is eluted from the beadsby resuspending the beads in 25 μL of sterile water or 0.1× TE Buffer.The reaction is placed back on the magnetic stand. Once the solution isclear, 20 μL of the supernatant is transferred to a new microfuge tube.

PCR enrichment of the adaptor ligated cfDNA is by mixing the followingcomponents:

Component Volume (μL) Adaptor ligated cfDNA 20 Universal PCR Primer (25μM) 2.5 Index Primer (25 μM) 2.5 Phusion High-Fidelity PCR Master Mix 25

The PCR enrichment is performed using the cycling conditions of 1 cycleat 98° C. for 30 seconds, 17 cycles of 98° C. for 10 seconds, 65° C. for30 seconds, and 72° C. for 30 seconds, followed by 1 cycle of 72° C. for5 minutes and a hold at 4° C.

Clean-up of the PCR enriched cfDNA is performed by adding 50 μL (1×) ofresuspended AMPure XP beads to the PCR enriched cfDNA reaction mixture.The AMPure beads are mixed into the solution on a vortex mixer or bypipetting up and down (e.g., 10 times or more). The reaction is placedon a magnetic stand and incubated at room temperature for 5 minutes oruntil the solution is clear. After the solution is clear, thesupernatant is removed and discarded. The beads are washed twice byadding 200 μL of 80% freshly prepared ethanol to the reaction while inthe magnetic stand. For each wash, the ethanol solution is added at roomtemperature for 30 seconds. The supernatant is removed and discarded.The beads are air dried for 10 minutes while the reaction is on themagnetic stand. The beads are resuspended in 30 μL of 0.1×TE. Thereaction is placed back on the magnetic stand and incubated at roomtemperature for until the solution is clear. 25 μL of the supernatant istransferred to a fresh, sterile container (e.g., microfuge tube). Theenriched cfDNA is diluted 20-fold with the addition of nuclease freewater

The enriched cfDNA is hybridized to an array comprising selector setprobes. The quantity of the circulating tumor DNA (ctDNA) is determinedusing array-based hybridization. An image of the array is obtained andthe quantity of the ctDNA is calculated based on the intensity signalson the array.

A report comprising the quantity of the ctDNA, the mutations found, anda list of anti-cancer therapies is provided to a physician. Based on thequantity of the ctDNA, the types of mutations found, and the list ofanti-cancer therapies, the physician provides a therapeutic regimen fortreating of the thyroid cancer in the subject.

TABLE 1 Fusion Pre-treatment Smoking No. of SNVs ALK/ ctDNA ctDNA TumorCase Age Sex Histology Stage TNM history (non-silent) Indels ROS1Partner (%) (pg/mL) (cc) P12 86 F SCC IA T1bN0M0 Heavy 6 (3) 1 ND ND 5.5P1  66 M Adeno IB T2aN0M0 Heavy 12 (3)  4 0.025 1.9 23.1 P16 82 F AdenoIB T2aN0M0 Heavy 26 (5)  2 0.019 2.5 22.5 P17 85 F Adeno IB T2aN0M0Heavy 2 (2) 0 ND ND 10.2 P13 90 F SCC IIB T3aN0M0 Heavy 5 (4) 0 1.78269.8 339.3 P2  61 M Large IIIA T3aN1M0 Heavy 12 (3)  1 0.896 64.7 23.1Cell P3  67 F Adeno IIIB T1bN3M0 Light 1 (1) 0 0.095 16.2 7.9 P14 55 MAdeno IIIB T1aN3M0 Heavy 8 (5) 0 0.05 10.2 5.2 P15 41 M Adeno IIIBT3N3M0 Light 25 (10) 1 0.58 108.1 121.8 P4  47 F Adeno IV T2aN2M1b Heavy3 (2) 0 0.039 2.1 12.4 P5  49 F Adeno IV T1bN0M1a None 4 (3) 0 3.2 143.882.1 P6  54 M Adeno IV T3N2M1b None 3 (2) 0 ALK KIF5B 1.0 350.2 NA P9 47 F Adeno IV T4N3M1a None 0 0 ALK EML4 0.04 3.8 66.2 ROS1 MKX, FYN P1035 F Adeno IIIA T4N0M0 None 0 0 ROS1 SLC34 — — — A2 P11 38 F Adeno IIIAT3N2M0 None 2 (1) 0 ROS1 CD74 — — — P7  50 M Adeno IV T1aN2M1b Light 0 0ALK EML4 — — — P8  48 F Adeno IV T4N0M1b None 1 (0) 0 ALK EML4 — — —Patient characteristics and pre-treatment CAPP-Seq monitoring results.ND, mutant DNA was not detected above background. NA, tumor volume couldnot be reliably assessed. Dashes, plasma sample not available. Smokinghistory, ≧20 pack years (Heavy), >0 and <20 pack years (Light).Additional details are provided in Tables 3, 4, 20 and 21.

TABLE 2 Coverage (unique LUAD & SCC patients; n = 407) Genomic Region %patients ≧1 Gene Chr Start (bp) End (bp) RI SNV AKT1 chr14 105246424105246553 7.7 0.25 BRAF chr7 140453074 140453193 66.7 2.21 BRAF chr7140481375 140481493 58.8 3.93 CDKN2A chr9 21970900 21971207 97.4 11.30CDKN2A chr9 21974475 21974826 19.9 13.02 CTNNB1 chr3 41266016 4126624426.2 14.00 EGFR chr7 55241613 55241736 24.2 14.25 EGFR chr7 5524241455242513 80.0 15.97 EGFR chr7 55248985 55249171 26.7 16.95 EGFR chr755259411 55259567 89.2 19.90 ERBB2 chr17 37880164 37880263 0.0 19.90ERBB2 chr17 37880978 37881164 21.4 20.88 HRAS chr11 533765 533944 16.721.38 HRAS chr11 534211 534322 26.8 22.11 KEAP1 chr19 10599867 1060004416.9 22.85 KEAP1 chr19 10600323 10600529 72.5 26.54 KEAP1 chr19 1060225210602938 36.4 31.45 KEAP1 chr19 10610070 10610709 28.1 34.64 KEAP1 chr1910597327 10597494 11.9 35.14 KRAS chr12 25380167 25380346 22.2 36.12KRAS chr12 25398207 25398318 500.0 46.93 MEK1 chr15 66727364 667275750.0 46.93 MET chr7 116411902 116412043 14.1 47.42 NFE2L2 chr2 178098732178098999 115.7 52.09 NOTCH1 chr9 139396723 139396940 4.6 52.09 NOTCH1chr9 139399124 139399556 0.0 52.09 NOTCH1 chr9 139390522 139392010 2.052.58 NOTCH1 chr9 139397633 139397782 0.0 52.58 NRAS chr1 115256420115256599 27.8 53.32 NRAS chr1 115258670 115258781 0.0 53.32 PIK3CA chr3178935997 178936122 150.8 55.28 PIK3CA chr3 178951881 178952152 14.756.02 PTEN chr10 89624226 89624305 12.5 56.27 PTEN chr10 8965378189653866 0.0 56.27 PTEN chr10 89685269 89685314 65.2 56.76 PTEN chr1089690802 89690846 0.0 56.76 PTEN chr10 89692769 89693008 20.8 57.49 PTENchr10 89711874 89712016 21.0 57.74 PTEN chr10 89717609 89717776 35.758.48 PTEN chr10 89720650 89720875 13.3 58.72 STK11 chr19 12069121207202 13.7 58.97 STK11 chr19 1218415 1218499 23.5 59.21 STK11 chr191219322 1219412 11.0 59.46 STK11 chr19 1220371 1220504 29.9 59.46 STK11chr19 1220579 1220716 29.0 59.46 STK11 chr19 1221211 1221339 31.0 59.46STK11 chr19 1221947 1222005 0.0 59.46 STK11 chr19 1222983 1223171 0.059.46 STK11 chr19 1226452 1226646 0.0 59.46 TP53 chr17 7577018 7577155405.8 64.86 TP53 chr17 7577498 7577608 450.5 70.27 TP53 chr17 75781767578289 342.1 73.71 TP53 chr17 7579311 7579590 110.7 76.66 TP53 chr177578370 7578554 367.6 83.54 REG1B chr2 79313937 79314056 83.3 83.78 TPTEchr21 10970008 10970062 72.7 84.28 CSMD3 chr8 113246593 113246706 70.284.77 TP53 chr17 7573926 7574033 83.3 85.50 FAM135B chr8 139151228139151339 71.4 86.00 U2AF1 chr21 44524424 44524512 56.2 86.24 THSD7Achr7 11501637 11501770 67.2 86.49 MLL3 chr7 151962122 151962294 63.686.73 EYA4 chr6 133849862 133849943 61.0 86.98 HCN1 chr5 4526719045267355 54.2 87.22 AKR1B10 chr7 134222945 134223029 58.8 87.71 SLC6A5chr11 20668379 20668480 49.0 87.96 DPP10 chr2 116525872 116525980 55.088.45 SCN7A chr2 167327124 167327216 43.0 88.70 SNTG1 chr8 5162144551621538 53.2 88.94 VPS13A chr9 79946925 79947029 47.6 89.19 IL1RAPL1chrX 29938065 29938211 47.6 89.43 CTNNA2 chr2 80085138 80085305 47.689.68 CSMD3 chr8 113323206 113323395 47.4 89.93 FAM5C chr1 190203501190203607 46.7 90.17 CACNA1E chr1 181708282 181708389 37.0 90.42KRTAP5-5 chr11 1651070 1651784 43.4 91.15 PDE1C chr7 31864480 3186460141.0 91.40 RYR2 chr1 237806626 237806747 41.0 91.65 NRXN1 chr2 5073363250733755 40.3 91.89 COL19A1 chr6 70637800 70637924 40.0 92.14 CSMD3 chr8113697634 113697961 39.6 92.38 LRP1B chr2 141665445 141665646 34.7 92.63GKN2 chr2 69173435 69173592 38.0 92.87 CD5L chr1 157805624 15780594537.3 93.12 SPTA1 chr1 158627266 158627484 36.5 93.37 DHX9 chr1 182812428182812569 35.2 93.61 ADAMTS20 chr12 43858393 43858535 35.0 93.86 NLRP4chr19 56382192 56382363 34.9 93.86 CDH18 chr5 19473334 19473825 34.694.35 MYH2 chr17 10450791 10450935 34.5 94.84 OR5L2 chr11 5559469455595630 32.0 94.84 OR4A15 chr11 55135359 55136394 30.9 94.84 OR6F1 chr1247875130 247876057 28.0 94.84 OR4C6 chr11 55432642 55433572 29.0 95.09OR2T4 chr1 248524882 248525929 31.5 95.09 FAM5C chr1 190067147 19006826431.3 95.09 PSG2 chr19 43575851 43576106 35.2 95.09 ITM2A chrX 7861843878618636 30.2 95.09 TNN chr1 175092535 175092799 45.3 95.09 GATA3 chr108105958 8106101 20.8 95.09 HCN1 chr5 45461947 45462109 30.7 95.09 OCA2chr15 28211835 28211968 44.8 95.09 CTNNA2 chr2 80816428 80816610 27.395.09 CNTN5 chr11 99715818 99715994 33.9 95.09 POM121L12 chr7 5310336453104255 31.4 95.09 LRRC7 chr1 70225887 70226076 26.3 95.09 CNTNAP5 chr2125530375 125530594 36.4 95.09 SLC4A10 chr2 162751188 162751335 33.895.09 SETD2 chr3 47142947 47143045 30.3 95.09 GFRAL chr6 5521605055216381 30.1 95.09 SORCS3 chr10 106927015 106927107 32.3 95.33 POTEGchr14 19553416 19553937 32.6 95.33 F9 chrX 138630521 138630650 30.895.58 SLC26A3 chr7 107416896 107416989 21.3 95.58 UNC5D chr8 3560604435606213 29.4 95.58 PDE4DIP chr1 144882775 144882881 37.4 95.58 MRPL1chr4 78870950 78871032 48.2 95.58 COL25A1 chr4 109784474 109784543 42.995.58 SPTA1 chr1 158650372 158650519 33.8 95.58 TNR chr1 175331798175331945 33.8 95.58 GALNT13 chr2 155157921 155158102 33.0 95.58 EIF3Echr8 109241298 109241424 39.4 95.58 SLC5A1 chr22 32445929 32446001 54.895.58 COASY chr17 40717000 40717065 45.5 95.58 TBX15 chr1 119467268119467440 40.5 95.58 PYHIN1 chr1 158908869 158909037 35.5 95.58 PSG5chr19 43690493 43690557 46.2 95.58 BTRC chr10 103290993 103291090 20.495.58 MDGA2 chr14 47324226 47324357 30.3 95.58 GUCY1A3 chr4 156629387156629446 33.3 95.58 HGF chr7 81386504 81386619 34.5 95.58 TIMD4 chr5156346467 156346552 34.9 95.58 AK5 chr1 77752625 77752812 31.9 95.58ODZ3 chr4 183245173 183245405 30.0 95.58 COL5A2 chr2 189927897 18992799630.0 95.58 NTM chr11 132180005 132180126 32.8 95.58 LTBP1 chr2 3350003133500157 39.4 95.58 PRSS1 chr7 142458405 142458565 31.1 95.58 CDKN2Achr9 21971001 21971207 125.6 95.58 CNGB3 chr8 87738758 87738885 31.395.58 SI chr3 164777689 164777815 31.5 95.58 SI chr3 164767578 16476766346.5 95.58 TMEM132D chr12 129822178 129822362 32.4 95.58 ASTN1 chr1176998769 176998877 27.5 95.58 SAGE1 chrX 134987410 134987551 42.3 95.58THSD7A chr7 11464322 11464459 36.2 95.58 ADAMTS12 chr5 33683963 3368416030.3 95.58 NRXN1 chr2 50463926 50464108 43.7 95.58 CSMD3 chr8 113562899113563102 34.3 95.58 CSMD3 chr8 113364644 113364763 41.7 95.58 EPB41L4Bchr9 112018415 112018504 22.2 95.58 POLR3B chr12 106820974 10682113624.5 95.58 ATP10B chr5 160097469 160097674 34.0 95.58 CSMD1 chr8 31652163165343 31.3 95.58 FBN2 chr5 127648325 127648487 30.7 95.58 EXOC5 chr1457684699 57684786 22.7 95.58 ANKRD304 chr10 37440987 37441049 47.6 95.58TRIML1 chr4 189065189 189065287 40.4 95.58 SPTA1 chr1 158631076158631199 32.3 95.58 POLDIP2 chr17 26684313 26684473 31.1 95.58 KLHL1chr13 70314525 70314688 30.5 95.58 TRIM58 chr1 248039201 248039791 23.795.58 GRIA3 chrX 122537262 122537370 27.5 95.58 CNOT4 chr7 135048605135048818 23.4 95.58 NAV3 chr12 78582388 78582557 23.5 95.58 NAV3 chr1278400198 78401225 21.4 95.58 TRPC5 chrX 111195270 111195648 21.1 95.58LRRC2 chr3 46592956 46593081 23.8 95.58 ADAMTS16 chr5 5239793 524003824.4 95.58 ACER2 chr9 19424697 19424839 21.0 95.58 AMOT chrX 112024113112024346 21.4 95.58 OBP2A chr9 138439716 138439827 26.8 95.58 INHBAchr7 41729247 41730140 19.0 95.58 INHBA chr7 41739584 41739972 7.7 95.58EPHA5 chr4 66189831 66189937 28.0 95.58 EPHA5 chr4 66197690 6619784612.7 95.58 EPHA5 chr4 66201649 66201843 10.3 95.58 EPHA5 chr4 6621377166213921 19.9 95.58 EPHA5 chr4 66217106 66217316 19.0 95.58 EPHA5 chr466218740 66218840 19.8 95.58 EPHA5 chr4 66230734 66230920 16.0 95.58EPHA5 chr4 66231649 66231775 23.6 95.58 EPHA5 chr4 66233058 6623315819.8 95.58 EPHA5 chr4 66242698 66242798 0.0 95.58 EPHA5 chr4 6627009166270194 19.2 95.58 EPHA5 chr4 66280001 66280161 6.2 95.58 EPHA5 chr466286158 66286283 0.0 95.58 EPHA5 chr4 66356094 66356430 14.8 95.58EPHA5 chr4 66361105 66361261 6.4 95.58 EPHA5 chr4 66467358 66468022 9.095.58 EPHA5 chr4 66509062 66509163 0.0 95.58 EPHA5 chr4 6653527966535460 5.5 95.58 EPHA3 chr3 89156892 89156992 0.0 95.58 EPHA3 chr389176340 89176441 19.6 95.58 EPHA3 chr3 89259009 89259670 9.1 95.58EPHA3 chr3 89390065 89390221 25.5 95.58 EPHA3 chr3 89390904 89391240 8.995.58 EPHA3 chr3 89444986 89445111 15.9 95.58 EPHA3 chr3 8944846789448656 5.3 95.58 EPHA3 chr3 89456418 89456521 0.0 95.58 EPHA3 chr389457198 89457299 0.0 95.58 EPHA3 chr3 89462290 89462416 23.6 95.58EPHA3 chr3 89468354 89468540 5.3 95.58 EPHA3 chr3 89478236 89478336 0.095.58 EPHA3 chr3 89480299 89480509 19.0 95.58 EPHA3 chr3 8949837489498524 6.6 95.58 EPHA3 chr3 89499326 89499520 10.3 95.58 EPHA3 chr389521613 89521769 19.1 95.58 EPHA3 chr3 89528546 89528652 9.3 95.58PTPRD chr9 8317857 8317958 19.6 95.58 PTPRD chr9 8319830 8319966 0.095.58 PTPRD chr9 8331581 8331736 6.4 95.58 PTPRD chr9 8338921 833904715.7 95.58 PTPRD chr9 8340342 8340469 7.8 95.58 PTPRD chr9 83410898341268 0.0 95.58 PTPRD chr9 8341692 8341978 7.0 95.58 PTPRD chr98375935 8376090 6.4 95.58 PTPRD chr9 8376606 8376726 8.3 95.58 PTPRDchr9 8389231 8389407 0.0 95.58 PTPRD chr9 8404536 8404660 0.0 95.58PTPRD chr9 8436590 8436690 9.9 95.58 PTPRD chr9 8437168 8437268 0.095.58 PTPRD chr9 8449724 8449837 26.3 95.58 PTPRD chr9 8454536 84546370.0 95.58 PTPRD chr9 8460410 8460571 18.5 95.58 PTPRD chr9 84654658465675 28.4 95.58 PTPRD chr9 8470989 8471090 9.8 95.58 PTPRD chr98484118 8484378 19.2 95.58 PTPRD chr9 8485226 8485327 0.0 95.58 PTPRDchr9 8485761 8486349 6.8 95.58 PTPRD chr9 8492861 8492979 8.4 95.58PTPRD chr9 8497204 8497305 9.8 95.58 PTPRD chr9 8499646 8499840 10.395.58 PTPRD chr9 8500753 8501059 9.8 95.58 PTPRD chr9 8504260 85044056.8 95.58 PTPRD chr9 8507300 8507434 7.4 95.58 PTPRD chr9 85178478518429 15.4 95.58 PTPRD chr9 8521276 8521546 18.5 95.58 PTPRD chr98523468 8523568 9.9 95.58 PTPRD chr9 8524924 8525035 8.9 95.58 PTPRDchr9 8526585 8526685 0.0 95.58 PTPRD chr9 8527298 8527399 19.6 95.58PTPRD chr9 8528590 8528779 21.1 95.58 PTPRD chr9 8633316 8633458 21.095.58 PTPRD chr9 8636698 8636844 13.6 95.58 PTPRD chr9 8733761 87338610.0 95.58 KDR chr4 55946107 55946330 4.5 95.58 KDR chr4 5594811555948215 0.0 95.58 KDR chr4 55948702 55948802 19.8 95.58 KDR chr455953773 55953925 19.6 95.58 KDR chr4 55955034 55955140 18.7 95.58 KDRchr4 55955540 55955640 0.0 95.58 KDR chr4 55955857 55955969 8.8 95.58KDR chr4 55956122 55956245 0.0 95.58 KDR chr4 55958782 55958882 19.895.58 KDR chr4 55960968 55961122 12.9 95.58 KDR chr4 55961737 5596183819.6 95.58 KDR chr4 55962395 55962509 8.7 95.58 KDR chr4 5596382855963933 28.3 95.58 KDR chr4 55964303 55964439 0.0 95.58 KDR chr455964863 55964970 18.5 95.58 KDR chr4 55968063 55968195 7.5 95.58 KDRchr4 55968528 55968675 13.5 95.58 KDR chr4 55970809 55971151 14.6 95.58KDR chr4 55971998 55972107 18.2 95.58 KDR chr4 55972853 55972977 8.095.58 KDR chr4 55973903 55974060 12.7 95.58 KDR chr4 55976569 5597673312.1 95.58 KDR chr4 55976820 55976935 8.6 95.58 KDR chr4 5597947055979648 11.2 95.58 KDR chr4 55980292 55980432 0.0 95.58 KDR chr455981040 55981209 5.9 95.58 KDR chr4 55981447 55981578 30.3 95.58 KDRchr4 55984770 55984967 0.0 95.58 KDR chr4 55987260 55987360 9.9 95.58KDR chr4 55991376 55991477 0.0 95.58 NTRK3 chr15 88420165 88420351 0.095.58 NTRK3 chr15 88423500 88423659 6.3 95.58 NTRK3 chr15 8842889588428995 0.0 95.58 NTRK3 chr15 88472421 88472665 4.1 95.58 NTRK3 chr1588476242 88476415 23.0 95.58 NTRK3 chr15 88483853 88483984 7.6 95.58NTRK3 chr15 88522575 88522694 0.0 95.58 NTRK3 chr15 88524456 885245910.0 95.58 NTRK3 chr15 88576087 88576276 10.5 95.58 NTRK3 chr15 8866950188669604 28.8 95.58 NTRK3 chr15 88670374 88670475 0.0 95.58 NTRK3 chr1588671903 88672003 0.0 95.58 NTRK3 chr15 88678331 88678628 23.5 95.58NTRK3 chr15 88679129 88679271 7.0 95.58 NTRK3 chr15 88679697 8867984013.9 95.58 NTRK3 chr15 88680634 88680792 0.0 95.58 NTRK3 chr15 8869054988690650 0.0 95.58 NTRK3 chr15 88726634 88726734 9.9 95.58 NTRK3 chr1588727442 88727543 9.8 95.58 RB1 chr13 48878048 48878185 0.0 95.58 RB1chr13 48881415 48881542 23.4 95.58 RB1 chr13 48916734 48916850 8.5 95.58RB1 chr13 48919215 48919335 8.3 95.58 RB1 chr13 48921929 48922030 0.095.58 RB1 chr13 48923075 48923175 0.0 95.58 RB1 chr13 48934152 4893426317.9 95.58 RB1 chr13 48936950 48937093 0.0 95.58 RB1 chr13 4893901848939118 0.0 95.58 RB1 chr13 48941629 48941739 27.0 95.58 RB1 chr1348942651 48942751 0.0 95.58 RB1 chr13 48947534 48947634 19.8 95.58 RB1chr13 48951053 48951170 0.0 95.58 RB1 chr13 48953707 48953808 19.6 95.58RB1 chr13 48954154 48954254 0.0 95.58 RB1 chr13 48954288 48954389 9.895.58 RB1 chr13 48955382 48955579 0.0 95.58 RB1 chr13 49027128 490272470.0 95.58 RB1 chr13 49030339 49030485 20.4 95.58 RB1 chr13 4903382349033969 6.8 95.58 RB1 chr13 49037866 49037971 0.0 95.58 RB1 chr1349039133 49039247 8.7 95.58 RB1 chr13 49039340 49039504 12.1 95.58 RB1chr13 49047460 49047561 0.0 95.58 RB1 chr13 49050836 49050979 0.0 95.58RB1 chr13 49051465 49051565 0.0 95.58 RB1 chr13 49054120 49054220 0.095.58 ERBB4 chr2 212248339 212248785 6.7 95.58 ERBB4 chr2 212251577212251875 10.0 95.58 ERBB4 chr2 212252643 212252743 0.0 95.58 ERBB4 chr2212285165 212285336 11.6 95.58 ERBB4 chr2 212286730 212286830 9.9 95.58ERBB4 chr2 212288879 212289026 6.8 95.58 ERBB4 chr2 212293120 2122932200.0 95.58 ERBB4 chr2 212295669 212295825 12.7 95.58 ERBB4 chr2 212426627212426813 5.3 95.58 ERBB4 chr2 212483901 212484000 0.0 95.58 ERBB4 chr2212488646 212488769 0.0 95.58 ERBB4 chr2 212495186 212495319 0.0 95.58ERBB4 chr2 212522465 212522566 19.6 95.58 ERBB4 chr2 212530047 2125302026.4 95.58 ERBB4 chr2 212537885 212537985 9.9 95.58 ERBB4 chr2 212543776212543909 7.5 95.58 ERBB4 chr2 212566691 212566891 10.0 95.58 ERBB4 chr2212568823 212568924 0.0 95.58 ERBB4 chr2 212570029 212570129 9.9 95.58ERBB4 chr2 212576774 212576901 7.8 95.58 ERBB4 chr2 212578259 2125783738.7 95.58 ERBB4 chr2 212587117 212587259 0.0 95.58 ERBB4 chr2 212589800212589919 16.7 95.58 ERBB4 chr2 212615346 212615446 0.0 95.58 ERBB4 chr2212652749 212652884 7.4 95.58 ERBB4 chr2 212812154 212812341 21.3 95.82ERBB4 chr2 212989476 212989628 13.1 95.82 ERBB4 chr2 213403163 2134032630.0 95.82 NTRK1 chr1 156785575 156785676 0.0 95.82 NTRK1 chr1 156811872156811985 0.0 95.82 NTRK1 chr1 156830726 156830938 0.0 95.82 NTRK1 chr1156834132 156834233 9.8 95.82 NTRK1 chr1 156834505 156834605 0.0 95.82NTRK1 chr1 156836685 156836786 0.0 95.82 NTRK1 chr1 156837895 1568380416.8 95.82 NTRK1 chr1 156838296 156838439 0.0 95.82 NTRK1 chr1 156841414156841547 0.0 95.82 NTRK1 chr1 156843424 156843751 3.0 95.82 NTRK1 chr1156844133 156844233 0.0 95.82 NTRK1 chr1 156844340 156844440 0.0 95.82NTRK1 chr1 156844697 156844800 0.0 95.82 NTRK1 chr1 156845311 15684545813.5 95.82 NTRK1 chr1 156845871 156846002 22.7 95.82 NTRK1 chr1156846191 156846364 11.5 95.82 NTRK1 chr1 156848913 156849154 16.5 95.82NTRK1 chr1 156849790 156849949 0.0 95.82 NTRK1 chr1 156851248 1568514340.0 95.82 NF1 chr17 29422307 29422407 0.0 95.82 NF1 chr17 2948300029483144 0.0 95.82 NF1 chr17 29486019 29486119 9.9 95.82 NF1 chr1729490203 29490394 5.2 95.82 NF1 chr17 29496908 29497015 9.3 95.82 NF1chr17 29508423 29508523 0.0 95.82 NF1 chr17 29508715 29508815 0.0 95.82NF1 chr17 29509525 29509683 6.3 95.82 NF1 chr17 29527439 29527613 17.195.82 NF1 chr17 29528054 29528177 0.0 95.82 NF1 chr17 29528415 295285160.0 95.82 NF1 chr17 29533257 29533389 0.0 95.82 NF1 chr17 2954146829541603 7.4 95.82 NF1 chr17 29546022 29546136 8.7 95.82 NF1 chr1729548867 29549008 7.0 95.82 NF1 chr17 29550461 29550585 0.0 95.82 NF1chr17 29552112 29552268 0.0 95.82 NF1 chr17 29553452 29553702 4.0 95.82NF1 chr17 29554222 29554322 0.0 95.82 NF1 chr17 29554532 29554632 9.995.82 NF1 chr17 29556042 29556483 4.5 95.82 NF1 chr17 29556852 295569927.1 95.82 NF1 chr17 29557277 29557400 8.1 95.82 NF1 chr17 2955785129557951 0.0 95.82 NF1 chr17 29559090 29559207 0.0 95.82 NF1 chr1729559717 29559899 10.9 95.82 NF1 chr17 29560019 29560231 4.7 95.82 NF1chr17 29562628 29562790 12.3 95.82 NF1 chr17 29562935 29563039 0.0 95.82NF1 chr17 29576001 29576137 0.0 95.82 NF1 chr17 29579936 29580037 0.095.82 NF1 chr17 29585361 29585520 0.0 95.82 NF1 chr17 29586048 295861489.9 95.82 NF1 chr17 29587386 29587533 13.5 95.82 NF1 chr17 2958872829588875 0.0 95.82 NF1 chr17 29592246 29592357 0.0 95.82 NF1 chr1729652837 29653270 4.6 95.82 NF1 chr17 29654516 29654857 8.8 95.82 NF1chr17 29657313 29657516 9.8 95.82 NF1 chr17 29661855 29662049 15.4 95.82NF1 chr17 29663350 29663491 14.1 95.82 NF1 chr17 29663652 29663932 0.095.82 NF1 chr17 29664385 29664600 4.6 95.82 NF1 chr17 29664817 296649179.9 95.82 NF1 chr17 29665042 29665157 0.0 95.82 NF1 chr17 2966572129665823 19.4 95.82 NF1 chr17 29667522 29667663 7.0 95.82 NF1 chr1729670026 29670153 15.6 95.82 NF1 chr17 29676137 29676269 15.0 95.82 NF1chr17 29677200 29677336 0.0 95.82 NF1 chr17 29679274 29679432 12.6 95.82NF1 chr17 29683477 29683600 0.0 95.82 NF1 chr17 29683977 29684108 7.695.82 NF1 chr17 29684286 29684387 9.8 95.82 NF1 chr17 29685497 296856406.9 95.82 NF1 chr17 29685959 29686060 0.0 95.82 NF1 chr17 2968750429687721 0.0 95.82 NF1 chr17 29701030 29701173 6.9 95.82 APC chr5112043414 112043579 0.0 95.82 APC chr5 112090587 112090722 0.0 95.82 APCchr5 112102014 112102115 9.8 95.82 APC chr5 112102885 112103087 9.995.82 APC chr5 112111325 112111434 9.1 95.82 APC chr5 112116486112116600 0.0 95.82 APC chr5 112128134 112128234 0.0 95.82 APC chr5112136975 112137080 0.0 95.82 APC chr5 112151191 112151290 0.0 95.82 APCchr5 112154662 112155041 2.6 95.82 APC chr5 112157590 112157690 0.095.82 APC chr5 112162804 112162944 0.0 95.82 APC chr5 112163614112163714 0.0 95.82 APC chr5 112164552 112164669 16.9 95.82 APC chr5112170647 112170862 0.0 95.82 APC chr5 112173249 112179823 3.5 96.07 ATMchr11 108098337 108098437 0.0 96.07 ATM chr11 108098502 108098615 8.896.07 ATM chr11 108099904 108100050 0.0 96.07 ATM chr11 108106396108106561 0.0 96.07 ATM chr11 108114679 108114845 0.0 96.07 ATM chr11108115514 108115753 4.2 96.07 ATM chr11 108117690 108117854 0.0 96.07ATM chr11 108119659 108119829 5.8 96.07 ATM chr11 108121427 1081217990.0 96.07 ATM chr11 108122563 108122758 0.0 96.07 ATM chr11 108123541108123641 9.9 96.07 ATM chr11 108124540 108124766 0.0 96.07 ATM chr11108126941 108127067 7.9 96.07 ATM chr11 108128207 108128333 0.0 96.07ATM chr11 108129707 108129807 0.0 96.07 ATM chr11 108137897 1081380695.8 96.07 ATM chr11 108139136 108139336 0.0 96.07 ATM chr11 108141781108141882 0.0 96.07 ATM chr11 108141977 108142133 0.0 96.07 ATM chr11108143246 108143346 0.0 96.07 ATM chr11 108143448 108143579 7.6 96.07ATM chr11 108150217 108150335 0.0 96.07 ATM chr11 108151721 1081518950.0 96.07 ATM chr11 108153436 108153606 11.7 96.07 ATM chr11 108154953108155200 4.0 96.07 ATM chr11 108158326 108158442 0.0 96.07 ATM chr11108159703 108159830 7.8 96.07 ATM chr11 108160328 108160528 5.0 96.07ATM chr11 108163345 108163520 0.0 96.07 ATM chr11 108164039 1081642040.0 96.07 ATM chr11 108165653 108165786 0.0 96.07 ATM chr11 108168011108168111 9.9 96.07 ATM chr11 108170440 108170612 5.8 96.07 ATM chr11108172374 108172516 0.0 96.07 ATM chr11 108173579 108173756 0.0 96.07ATM chr11 108175401 108175579 11.2 96.07 ATM chr11 108178617 1081787170.0 96.07 ATM chr11 108180886 108181042 0.0 96.07 ATM chr11 108183131108183231 9.9 96.07 ATM chr11 108186543 108186644 0.0 96.07 ATM chr11108186737 108186840 9.6 96.07 ATM chr11 108188099 108188248 0.0 96.07ATM chr11 108190680 108190785 0.0 96.07 ATM chr11 108192027 1081921470.0 96.07 ATM chr11 108196036 108196271 4.2 96.07 ATM chr11 108196784108196952 0.0 96.07 ATM chr11 108198371 108198485 0.0 96.07 ATM chr11108199747 108199965 4.6 96.07 ATM chr11 108200940 108201148 0.0 96.07ATM chr11 108202170 108202284 0.0 96.07 ATM chr11 108202605 1082027640.0 96.07 ATM chr11 108203488 108203627 0.0 96.07 ATM chr11 108204603108204704 9.8 96.07 ATM chr11 108205695 108205836 21.1 96.07 ATM chr11108206571 108206688 8.5 96.07 ATM chr11 108213948 108214098 0.0 96.07ATM chr11 108216469 108216635 0.0 96.07 ATM chr11 108217998 1082180999.8 96.07 ATM chr11 108224492 108224607 8.6 96.07 ATM chr11 108225519108225619 0.0 96.07 ATM chr11 108235808 108235945 7.2 96.07 ATM chr11108236051 108236235 10.8 96.07 FGFR4 chr5 176516598 176516699 0.0 96.07FGFR4 chr5 176517390 176517654 3.8 96.07 FGFR4 chr5 176517735 1765178369.8 96.07 FGFR4 chr5 176517938 176518105 0.0 96.07 FGFR4 chr5 176518685176518809 0.0 96.07 FGFR4 chr5 176519321 176519512 0.0 96.07 FGFR4 chr5176519646 176519785 0.0 96.07 FGFR4 chr5 176520138 176520552 4.8 96.07FGFR4 chr5 176520654 176520776 0.0 96.07 FGFR4 chr5 176522330 1765224418.9 96.07 FGFR4 chr5 176522533 176522724 0.0 96.07 FGFR4 chr5 176523057176523180 0.0 96.07 FGFR4 chr5 176523272 176523373 0.0 96.07 FGFR4 chr5176523604 176523742 0.0 96.07 FGFR4 chr5 176524292 176524398 0.0 96.07FGFR4 chr5 176524527 176524677 0.0 96.07 ALK chr2 29446207 29448431 — —ROS1 chr6 117641031 117658503 — — RET chr10 43606655 43612179 — — PDGFRAchr4 55140698 55141140 — — FGFR1 chr8 38275746 38277253 — —

TABLE 3 Volume DNA Expected Total of Library mass haploid cfDNA plasmamass used genome concen- used used Sample description/ for copiestration in for for Sample patient (P#)/ Sample library (330 × plasmalibrary capture count healthy control (C#) source (ng) per ng) (ng/mL)(mL) (ng) 1 H3122 0.1% into HCC78 Cell line 128 42240 111 2 H3122 1%into HCC78 Cell line 128 42240 111 3 H3122 10% into HCC78 Cell line 12842240 111 4 H3122100% Cell line 128 42240 111 5 HCC78 100% Cell line 12842240 111 6 HCC78 10% into C1 Cell line/ 128 42240 83.3 plasma DNA 4cycles plasma DNA 7 HCC78 10% into C1 Cell line/ 1 330 83.3 plasma DNA 8cycles plasma SigmaWGA DNA 8 HCC78 10% into C1 Cell line/ 32 10560 83.3plasma DNA 6 cycles plasma DNA 9 HCC78 10% into C1 Cell line/ 32 1056083.3 plasma DNA 8 cycles plasma NEBNextOvernightBead DNA 10 HCC78 10%into C1 Cell line/ 32 10560 83.3 plasma DNA 8 cycles plasma OrigNEBNextDNA 15 min Lig 11 HCC78 10% into C1 Cell line/ 4 1320 83.3 plasma DNA 4ng plasma 9 cycles DNA 12 HCC78 0.025% into C1 Cell line/ 32 10560 83.3plasma DNA plasma DNA 13 HCC78 0.05% into C1 Cell line/ 32 10560 83.3plasma DNA plasma DNA 14 HCC78 0.1% into C1 Cell line/ 32 10560 83.3plasma DNA plasma DNA 15 HCC78 0.5% into C1 Cell line/ 32 10560 83.3plasma DNA plasma DNA 16 HCC78 1% into C1 Cell line/ 32 10560 83.3plasma DNA plasma DNA 17 P1 PBL 500 165000 83.3 18 P2 PBL 500 16500083.3 19 P3 PBL 500 165000 83.3 20 P4 PBL 500 165000 83.3 21 P5 PBL 500165000 83.3 22 P6 PBL 500 165000 83.3 23 P7 PBL 500 165000 83.3 24 P8PBL 500 165000 83.3 25 P9 PBL 500 165000 83.3 26 P10 PBL 400 132000 83.327 P11 PBL 500 165000 83.3 28 P12 PBL 200 66000 83.3 29 P13 PBL 20066000 83.3 30 P14 PBL 200 66000 83.3 31 P15 PBL 200 66000 83.3 32 P16PBL 200 66000 83.3 33 P17 PBL 200 66000 83.3 34 P1 Tumor 500 165000 83.335 P2 Tumor 500 165000 83.3 36 P3 Tumor 500 165000 83.3 37 P4 Tumor 20066000 83.3 38 P5 Tumor 100 33000 83.3 39 P6 Tumor 1000 330000 83.3 40 P7Tumor 500 165000 83.3 41 P8 Tumor 500 165000 83.3 42 P9 Tumor 69 2277083.3 43 P10 Tumor 500 165000 83.3 44 P11 Tumor 500 165000 83.3 45 P12Tumor 125 41196 83.3 46 P13 Tumor 5 1516 83.3 47 P14 Tumor 125 4119783.3 48 P15 Tumor 4 1427 83.3 49 P16 Tumor 12 3872 83.3 50 P17 Tumor 9731904 83.3 51 C1 Plasma DNA 32 10560 12.49 2.56 83.3 52 C2 Plasma DNA 2793 14.24 0.17 83.3 53 C3 Plasma DNA 37 12218 7.82 4.73 83.3 54 C4Plasma DNA 1 375 6.64 0.17 83.3 55 C5 Plasma DNA 21 6834 14.44 1.43 83.356 P1 time point 1 Plasma DNA 13 4290 7.33 1.77 83.3 57 P1 time point 2Plasma DNA 7 2310 7.52 0.93 83.3 58 P1 time point 3 Plasma DNA 36 1175519.87 1.79 83.3 59 P2 time point 1 Plasma DNA 13 4290 7.22 1.80 83.3 60P2 time point 2 Plasma DNA 16 5280 10.93 1.46 83.3 61 P2 time point 3Plasma DNA 35 11462 13.12 2.65 83.3 62 P3 time point 1 Plasma DNA 154950 17.17 0.87 83.3 63 P3 time point 2 Plasma DNA 16 5280 12.84 1.2583.3 64 P4 time point 1 Plasma DNA 10 3300 5.40 1.85 83.3 65 P4 timepoint 2 Plasma DNA 16 5280 18.19 0.88 83.3 66 P5 time point 1 Plasma DNA9 2970 4.49 2.00 83.3 68 P5 time point 2 Plasma DNA 29 9549 37.06 0.7883.3 67 P5 time point 3 Plasma DNA 15 4950 5.37 2.79 83.3 69 P6 timepoint 1 Plasma DNA 17 5610 35.11 0.48 83.3 70 P6 time point 2 Plasma DNA20 6600 85.87 0.23 83.3 71 P9 time point 1 Plasma DNA 12 3960 9.22 1.3083.3 72 P9 time point 2 Plasma DNA 17 5610 11.38 1.49 83.3 73 P9 timepoint 3 Plasma DNA 16 5280 10.41 1.54 83.3 74 P9 time point 4 Plasma DNA35 11622 19.42 1.81 83.3 75 P9 time point 5 Plasma DNA 36 11775 33.701.06 83.3 76 P12 time point 1 Plasma DNA 17 5507 11.03 1.51 83.3 77 P12time point 2 Plasma DNA 28 9230 15.57 1.80 83.3 78 P13 time point 1Plasma DNA 25 8291 15.18 1.65 83.3 79 P13 time point 2 Plasma DNA 155043 9.24 1.65 83.3 80 P14 time point 1 Plasma DNA 17 5716 20.36 0.8583.3 81 P14 time point 2 Plasma DNA 35 11596 27.49 1.28 83.3 82 P15 timepoint 1 Plasma DNA 25 8111 18.57 1.32 83.3 83 P15 time point 2 PlasmaDNA 31 10308 17.86 1.75 83.3 84 P15 time point 3 Plasma DNA 7 2305 5.281.32 83.3 85 P15 time point 4 Plasma DNA 23 7525 5.74 3.97 83.3 86 P15time point 5 Plasma DNA 8 2517 2.88 2.65 83.3 87 P16 time point 1 PlasmaDNA 17 5688 13.02 1.32 83.3 88 P16 time point 2 Plasma DNA 6 2089 10.140.62 83.3 89 P16 time point 3 Plasma DNA 32 10579 17.49 1.83 83.3 90 P17time point 1 Plasma DNA 12 4056 9.28 1.32 83.3

TABLE 4 Total plasma ctDNA Patient Plasma DNA ctDNA detection numbertime point % ctDNA^(a) (ng/mL) (pg/mL) index^(b) P12 1 ND 11.032 ND NSP12 2 ND 15.571 ND NS P1 1 0.025 7.326 1.854 0.005 P1 2 ND 7.520 ND NSP1 3 ND 19.869 ND NS P16 1 0.019 13.023 2.474 0.05 P16 2 ND 10.140 ND NSP16 3 ND 17.492 ND NS P17 1 ND 9.285 ND NS P13 1 1.777 15.184 269.821<0.0001 P13 2 ND 9.237 ND NS P2 1 0.896 7.221 64.698 <0.0001 P2 2 0.03810.927 4.152 0.03 P2 3 ND 13.120 ND NS P3 1 0.095 17.171 16.237 0.009 P32 ND 12.841 ND NS P14 1 0.050 20.356 10.179 0.02 P14 2 0.042 27.49111.416 0.02 P15 1 0.582 18.568 108.117 3.2E−05 P15 2 ND 17.859 ND NS P153 ND 5.276 ND NS P15 4 0.421 5.742 24.201 1.7E−06 P15 5 0.855 2.88124.639 0.0001 P4 1 0.039 5.400 2.125 0.04 P4 2 ND 18.191 ND NS P5 13.201 4.491 143.781 <0.0001 P5 2 0.074 37.064 27.557 0.02 P5 3 0.3515.372 18.861 0.0006 P6 1 0.998 35.100 350.190 ~0 P6 2 0.230 85.900197.951 ~0 P9 1 0.042 9.221 3.828 ~0 P9 2 0.005 11.383 0.585 ~0 P9 30.050 10.406 5.184 ~0 P9 4 0.019 19.419 3.615 ~0 P9 5 ND 33.697 ND NS^(a)Mean fraction across all SNV/indel reporters if present, or fusionsif no other reporter types present. The subclonal T790M reporteridentified in P5 was excluded. ^(b)Analogous to false positive rate

TABLE 5 Mutant Mutant Mutant Mutant Ref. allele Total ctDNA allele TotalctDNA allele Total ctDNA Case allele allele Chr Position depth depth (%)depth depth (%) depth depth (%) Time point 1 Time point 2 Time point 3P12 T C chr 4 55973786 0 1508 0.000 2 2104 0.095 — — — P12 T G chr 6117650296 1 5165 0.019 2 6148 0.033 — — — P12 G T chr 7 41729291 0 37730.000 0 4634 0.000 — — — P12 T A chr 9 8471102 0 3637 0.000 0 4246 0.000— — — P12 G T chr 12 25380276 0 3633 0.000 0 4186 0.000 — — — P12 A Cchr 19 10602473 0 1873 0.000 0 2399 0.000 — — — P12 −C   T chr 177577057 0 3451 0.000 0 3779 0.000 — — — P1  A G chr 1 156785560 0 45720.000 3 6202 0.048 0 5220 0.000 P1  T G chr 1 157806043 0 1838 0.000 02266 0.000 0 1902 0.000 P1  G C chr 1 248525206 0 2828 0.000 0 45290.000 0 3327 0.000 P1  C T chr 2 33500291 1 943 0.106 0 943 0.000 0 9350.000 P1  A C chr 4 55946307 0 6856 0.000 0 8817 0.000 0 6279 0.000 P1 G A chr 4 55963949 0 5742 0.000 0 7335 0.000 2 5766 0.035 P1  A C chr 455968672 0 5856 0.000 0 7431 0.000 0 6376 0.000 P1  C T chr 6 1176421460 5266 0.000 4 6849 0.058 0 5407 0.000 P1  T G chr 9 8376700 3 55350.054 0 7322 0.00 0 6196 0.000 P1  T C chr 9 8733625 1 827 0.121 0 13980.000 0 1110 0.000 P1  T G chr 10 43611663 0 3722 0.000 0 4565 0.000 06741 0.000 P1  T G chr 15 88522525 1 4919 0.020 4 6736 0.059 12 56930.211 P1  +G   C chr 17 7578474 0 1762 0.000 0 2373 0.000 5 4578 0.109P1  −A   G chr 17 29552244 1 4484 0.022 0 6485 0.000 0 4640 0.000 P1 +T   C chr 17 29553484 0 3657 0.000 0 4713 0.000 0 3618 0.000 P1  −T   Cchr 17 29592185 3 3694 0.081 0 3247 0.000 0 3692 0.000 P16 A G chr 1156843429 7 3107 0.225 1 3492 0.029 0 3602 0.000 P16 T C chr 1 1817082910 5009 0.000 0 6962 0.000 4 6865 0.058 P16 A C chr 1 24852532 0 44840.000 0 5927 0.000 0 5948 0.000 P16 A C chr 2 125530343 0 5051 0.000 06591 0.000 2 6029 0.033 P16 A C chr 2 212530083 0 5112 0.000 1 59860.017 0 6462 0.000 P16 C T chr 2 212587119 0 5929 0.000 1 7481 0.013 07205 0.000 P16 T G chr 4 55958900 0 4585 0.000 6 5818 0.103 0 5664 0.000P16 C T chr 4 55962358 1 4558 0.022 0 6406 0.000 1 6077 0.016 P16 A Cchr 4 55968588 0 6084 0.000 0 8376 0.000 1 8537 0.012 P16 G A chr 455970963 0 5646 0.000 0 7604 0.000 0 7359 0.000 P16 A C chr 4 55971241 01562 0.000 0 2209 0.000 0 1952 0.000 P16 T G chr 5 19473838 0 3180 0.0000 4028 0.000 1 4127 0.024 P16 A G chr 5 112176654 9 5308 0.170 0 64810.000 0 5211 0.000 P16 T G chr 5 176520134 4 4790 0.084 1 5207 0.019 05946 0.000 P16 T G chr 7 11501543 0 2141 0.000 0 2950 0.000 0 3026 0.000P16 A C chr 7 53103357 0 2252 0.000 0 2737 0.000 1 2816 0.036 P16 T Cchr 7 116411990 0 5193 0.000 0 7080 0.000 0 6466 0.000 P16 A C chr 1043606641 0 3519 0.000 0 4261 0.000 0 4521 0.000 P16 A G chr 11 534195 12729 0.037 0 3262 0.000 0 3629 0.000 P16 G C chr 11 108143456 0 53080.000 0 6992 0.000 0 6833 0.000 P16 A C chr 12 25398284 0 4346 0.000 35866 0.051 0 5458 0.000 P16 A C chr 13 48947619 0 4639 0.000 1 62360.016 0 4765 0.000 P16 T C chr 13 70314492 0 2414 0.000 0 2752 0.000 02261 0.000 P16 A T chr 13 70314809 0 731 0.000 0 610 0.000 0 564 0.000P16 C G chr 15 88472337 0 2467 0.000 0 3236 0.000 0 3274 0.000 P16 A Cchr 17 7578132 0 2568 0.000 0 3369 0.000 0 3492 0.000 P16 +T   A chr 2212295977 0 483 0.000 0 356 0.000 0 302 0.000 P16 −C   T chr 19 12206380 2848 0.000 0 3186 0.000 0 4066 0.000 P17 T G chr 7 81386606 1 45240.022 — — — — — — P17 A C chr 12 25398285 0 4165 0.000 — — — — — — P13 TC chr 1 190067540 202 5609 3.601 7 6967 0.100 — — — P13 T C chr 545461969 147 5251 2.799 0 6568 0.000 — — — P13 G C chr 8 38276015 7 59370.118 0 8357 0.000 — — — P13 T C chr 15 88483904 1 5854 0.017 4 75280.053 — — — P13 T C chr 17 7577538 93 3962 2.347 3 5035 0.060 — — — P2 A C chr 2 50463926 49 6724 0.729 0 4981 0.000 0 5636 0.000 P2  G A chr 389457148 40 4838 0.827 0 4311 0.000 0 4114 0.000 P2  T G chr 3 894682865 4667 0.107 2 3625 0.055 6 3411 0.176 P2  T A chr 3 89480240 15 50730.296 0 4321 0.000 0 3984 0.000 P2  T A chr 4 66189669 4 950 0.421 51436 0.348 0 1237 0.000 P2  T G chr 4 66242868 16 2107 0.759 0 16550.000 0 1879 0.000 P2  A C chr 5 176522747 46 2220 2.072 0 1377 0.000 03196 0.000 P2  C T chr 6 117648229 70 7819 0.895 0 5985 0.000 0 59510.000 P2  A C chr 12 78400637 35 7907 0.443 1 6326 0.016 1 6402 0.016P2  T G chr 12 78400910 106 8211 1.291 1 6289 0.016 2 6260 0.032 P2  T Cchr 17 7577551 112 5629 1.990 2 3814 0.052 2 4934 0.041 P2  T G chr 191207247 15 1124 1.335 0 747 0.000 0 1214 0.000 P2  +A   C chr 2 7931410016 3280 0.488 0 2390 0.000 0 2299 0.000 P3  A C chr 17 7578253 6 63450.095 0 8583 0.000 — — — P14 C A chr 1 156841521 0 7377 0.000 0 50430.000 — — — P14 T G chr 3 89176334 0 4981 0.000 0 4471 0.000 — — — P14 AG chr 7 55249159 6 9223 0.065 1 6567 0.015 — — — P14 G T chr 7 552595151 7207 0.014 0 5418 0.000 — — — P14 T C chr 10 43607789 0 7552 0.000 15382 0.019 — — — P14 C T chr 17 7577545 0 6379 0.000 4 4773 0.084 — — —P14 T C chr 17 29553484 16 4983 0.321 8 3728 0.215 — — — P14 G C chr 191223125 0 5804 0.000 0 3984 0.000 — — — P15 T G chr 1 70226008 53 53170.997 0 6580 0.000 3 7204 0.042 P15 A C chr 1 144882833 30 11651 0.257 012602 0.000 0 15616 0.000 P15 A C chr 1 190203515 0 3976 0.000 0 50110.000 0 5485 0.000 P15 A C chr 1 248525334 32 5748 0.557 5 5359 0.093 17423 0.013 P15 A C chr 2 155157911 0 4151 0.000 0 5195 0.000 0 62950.000 P15 A G chr 2 212495103 10 1941 0.515 0 2439 0.000 1 2469 0.041P15 T G chr 3 89528742 16 2224 0.719 0 2523 0.000 1 2776 0.036 P15 T Gchr 4 55979517 197 7397 2.663 1 7458 0.013 0 10521 0.000 P15 A C chr 466189751 0 2556 0.000 0 3448 0.000 0 4235 0.000 P15 A C chr 4 66233002 6981 0.612 0 1212 0.000 0 1542 0.000 P15 A C chr 4 66233003 6 1027 0.5840 1258 0.000 0 1579 0.000 P15 T G chr 4 66233146 59 4970 1.187 0 56440.000 0 5923 0.000 P15 A C chr 5 176523126 59 5192 1.136 0 4356 0.000 47533 0.053 P15 A C chr 5 176524647 1 6308 0.016 0 5473 0.000 0 71790.000 P15 A C chr 7 41729339 33 5544 0.595 0 5817 0.000 0 8610 0.000 P15A C chr 8 87738607 0 744 0.000 0 1531 0.000 0 1094 0.000 P15 A C chr 8113563115 34 4123 0.825 0 4571 0.000 0 4569 0.000 P15 A C chr 9 852871656 6479 0.864 6 6339 0.095 0 8990 0.000 P15 A T chr 9 138439735 56 54971.019 0 5288 0.000 0 7310 0.000 P15 A C chr 10 43608292 21 5832 0.360 04912 0.000 0 7629 0.000 P15 T C chr 10 43608755 5 6687 0.075 1 67720.015 0 10118 0.000 P15 A C chr 11 55135855 63 5692 1.107 0 5984 0.000 09570 0.000 P15 T C chr 12 25398284 27 3573 0.756 1 4691 0.021 0 51930.000 P15 T C chr 13 48954333 0 2498 0.000 0 3674 0.000 0 3696 0.000 P15T G chr 13 48954451 1 2233 0.045 0 3214 0.000 0 3319 0.000 P15 +T   Gchr 17 29533514 4 1758 0.228 0 2705 0.000 0 2333 0.000 P4  T C chr 2212248555 6 7623 0.079 5 10563 0.047 — — — P4  T C chr 12 25398281 05359 0.000 0 9389 0.000 — — — P5  T C chr 7 55249071 42 4736 0.887 05978 0.000 10 5597 0.179 P5  G T chr 7 55259515 503 11349 4.432 12 59550.202 58 12222 0.475 P5  A G chr 11 55135338 86 4063 2.117 0 2802 0.00010 4798 0.208 P5  T C chr 17 7577097 227 7429 3.056 1 4643 0.022 36 97230.370 P6  A G chr 12 78400791 84 13970 0.601 28 10128 0.276 — — — P6  TG chr 12 129822187 78 8680 0.899 9 6604 0.136 — — — P6  A G chr 177578275 140 9376 1.493 22 7897 0.279 — — — P6* KIF — chr 10/ — 28 150060.187/ 2 9989 0.02/ — — — 5B- chr 2 1.56 0.167 ALK P9  EML — chr 2/ — 010688 0.000 0 13647 0.000 0 13521 0.000 4- chr 2 ALK P9  FYN- — chr 6/ —0 9261 0.000 0 6826 0.000 2 10693 0.019 ROS chr 6 1 P9  ROS- — chr 6/ —10 8029 0.125 1 6485 0.015 13 9943 0.131 1- chr 10 MKX P12 T C chr 455973786 — — — — — — P12 T G chr 6 117650296 — — — — — — P12 G T chr 741729291 — — — — — — P12 T A chr 9 8471102 — — — — — — P12 G T chr 1225380276 — — — — — — P12 A C chr 19 10602473 — — — — — — P12 −C   T chr17 7577057 — — — — — — P1  A G chr 1 156785560 — — — — — — P1  T G chr 1157806043 — — — — — — P1  G C chr 1 248525206 — — — — — — P1  C T chr 233500291 — — — — — — P1  A C chr 4 55946307 — — — — — — P1  G A chr 455963949 — — — — — — P1  A C chr 4 55968672 — — — — — — P1  C T chr 6117642146 — — — — — — P1  T G chr 9 8376700 — — — — — — P1  T C chr 98733625 — — — — — — P1  T G chr 10 43611663 — — — — — — P1  T G chr 1588522525 — — — — — — P1  +G   C chr 17 7578474 — — — — — — P1  −A   Gchr 17 29552244 — — — — — — P1  +T   C chr 17 29553484 — — — — — — P1 −T   C chr 17 29592185 — — — — — — P16 A G chr 1 156843429 — — — — — —P16 T C chr 1 181708291 — — — — — — P16 A C chr 1 248525326 — — — — — —P16 A C chr 2 125530343 — — — — — — P16 A C chr 2 212530083 — — — — — —P16 C T chr 2 212587119 — — — — — — P16 T G chr 4 55958900 — — — — — —P16 C T chr 4 55962358 — — — — — — P16 A C chr 4 55968588 — — — — — —P16 G A chr 4 55970963 — — — — — — P16 A C chr 4 55971241 — — — — — —P16 T G chr 5 19473838 — — — — — — P16 A G chr 5 112176654 — — — — — —P16 T G chr 5 176520134 — — — — — — P16 T G chr 7 11501543 — — — — — —P16 A C chr 7 53103357 — — — — — — P16 T C chr 7 116411990 — — — — — —P16 A C chr 10 43606641 — — — — — — P16 A G chr 11 534195 — — — — — —P16 G C chr 11 108143456 — — — — — — P16 A C chr 12 25398284 — — — — — —P16 A C chr 13 48947619 — — — — — — P16 T C chr 13 70314492 — — — — — —P16 A T chr 13 70314809 — — — — — — P16 C G chr 15 88472337 — — — — — —P16 A C chr 17 7578132 — — — — — — P16 +T   C chr 2 212295977 — — — — —— P16 −C   T chr 19 1220638 — — — — — — P17 T G chr 7 81386606 — — — — —— P17 A C chr 12 25398285 — — — — — — P13 T C chr 1 190067540 — — — — —— P13 T C chr 5 45461969 — — — — — — P13 G C chr 8 38276015 — — — — — —P13 T C chr 15 88483904 — — — — — — P13 T C chr 17 7577538 — — — — — —P2  A C chr 2 50463926 — — — — — — P2  G A chr 3 89457148 — — — — — —P2  T G chr 3 89468286 — — — — — — P2  T A chr 3 89480240 — — — — — —P2  T A chr 4 66189669 — — — — — — P2  T G chr 4 66242868 — — — — — —P2  A C chr 5 176522747 — — — — — — P2  C T chr 6 117648229 — — — — — —P2  A C chr 12 78400637 — — — — — — P2  T G chr 12 78400910 — — — — — —P2  T C chr 17 7577551 — — — — — — P2  T G chr 19 1207247 — — — — — —P2  +A   C chr 2 79314100 — — — — — — P3  A C chr 17 7578253 — — — — — —P14 C A chr 1 156841521 — — — — — — P14 T G chr 3 89176334 — — — — — —P14 A G chr 7 55249159 — — — — — — P14 G T chr 7 55259515 — — — — — —P14 T C chr 10 43607789 — — — — — — P14 C T chr 17 7577545 — — — — — —P14 T C chr 17 29553484 — — — — — — P14 G C chr 19 1223125 — — — — — —P15 T G chr 1 70226008 33 5346 0.617 124 6200 2.000 P15 A C chr 1144882833 23 9807 0.235 117 12719 0.920 P15 A C chr 1 190203515 0 38700.000 0 3965 0.000 P15 A C chr 1 248525334 27 4232 0.638 56 5397 1.038P15 A C chr 2 155157911 0 4146 0.000 0 4508 0.000 P15 A G chr 2212495103 6 1796 0.334 0 2025 0.00 P15 T G chr 3 89528742 21 1741 1.20625 2118 1.180 P15 T G chr 4 55979517 84 6351 1.323 219 7158 3.060 P15 AC chr 4 66189751 0 2590 0.000 0 2706 0.000 P15 A C chr 4 66233002 10 7591.318 0 852 0.000 P15 A C chr 4 66233003 10 791 1.264 0 868 0.000 P15 TG chr 4 66233146 24 4571 0.525 45 4578 0.983 P15 A C chr 5 176523126 273904 0.692 111 4798 2.313 P15 A C chr 5 176524647 0 4637 0.000 0 58640.000 P15 A C chr 7 41729339 16 4749 0.337 27 5865 0.460 P15 A C chr 887738607 1 847 0.118 12 1098 1.093 P15 A C chr 8 113563115 4 3404 0.11891 3470 2.622 P15 A C chr 9 8528716 17 5373 0.316 85 6082 1.398 P15 A Tchr 9 138439735 1 4332 0.023 19 5349 0.355 P15 A C chr 10 43608292 23998 0.050 52 4959 1.049 P15 T C chr 10 43608755 4 5518 0.072 24 64100.374 P15 A C chr 11 55135855 13 4702 0.276 105 5200 2.019 P15 T C chr12 25398284 2 3896 0.051 42 3951 1.063 P15 T C chr 13 48954333 0 25910.000 0 2786 0.000 P15 T G chr 13 48954451 24 2204 1.089 7 2633 0.266P15 +T   G chr 17 29533514 5 1618 0.309 0 1481 0.000 P4  T C chr 2212248555 — — — — — — P4  T C chr 12 25398281 — — — — — — P5  T C chr 755249071 — — — — — — P5  G T chr 7 55259515 — — — — — — P5  A G chr 1155135338 — — — — — — P5  T C chr 17 7577097 — — — — — — P6  A G chr 1278400791 — — — — — — P6  T G chr 12 129822187 — — — — — — P6  A G chr 177578275 — — — — — — P6* KIF — chr 10/ — — — — — — — 5B- chr 2 ALK P9 EML — chr 2/ — 0 9837 0.000 0 8667 0.000 4- chr 2 ALK P9  FYN- — chr 6/— 2 7700 0.026 0 7483 0.000 ROS chr 6 1 P9  ROS — chr 6/ — 2 6695 0.0300 6186 0.000 1- chr 10 MKX *By comparing to the mean fraction of SNVreporters in this tumor, the capture efficiency of this fusion wasestimated to be 12%. The % ctDNA of this fusion was therefore normalizedby dividing it by 0.12. ctDNA concentrations pre- and post-adjustmentare shown separated by a forward slash.

TABLE 6 Chromosome Start (bp) End (bp) Gene chr3 178935997 178936122P1K3CA chr3 178951909 178952140 P1K3CA chr17 7578369 7578555 TP53 chr177578176 7578289 TP53 chr17 7577018 7577155 TP53 chr17 7577498 7577608TP53 chr10 8115700 8115987 GATA3 chr6 170871003 170871217 TBP chr177579310 7579537 TP53 chr3 178921508 178921607 PIK3CA chr10 81114358111561 GATA3 chr9 141107487 141107586 FAM157B chr21 36252853 36253010RUNX1 chr16 68862076 68862207 CDH1 chr3 178927973 178928126 PIK3CA chr177573926 7574033 TP53 chr3 178916822 178916947 PIK3CA chr16 6884558668845763 CDH1 chr16 67070541 67070658 CBFB chr16 68835595 68835782 CDH1chr16 68844099 68844244 CDH1 chr2 46707798 46707897 TMEM247 chr1089692779 89693004 PTEN chr17 37880164 37880263 ERBB2 chrX 135960073135960245 RBMX chr3 178938773 178938945 PIK3CA chr5 56160564 56160762MAP3K1 chr6 26031882 26032137 HIST1H3B chr2 198266708 198266854 SF3B1chr2 129075869 129075968 HS6ST1 chr12 115118686 115118896 TBX3 chr556176937 56177100 MAP3K1 chr2 110301729 110301828 SEPT10 chr19 4945894349459090 BAX chr16 68772199 68772314 CDH1 chr10 51853598 51853697 FAM21Achr16 68855913 68856123 CDH1 chr16 68849435 68849649 CDH1 chr20 2963261029632721 FRG1B chr16 68842595 68842751 CDH1 chr14 23523714 23523882CDH24 chr9 116187612 116187711 C9orf43 chr1 120611934 120612033 NOTCH2chr17 7576839 7576938 TP53 chr16 68842326 68842470 CDH1 chr19 81309178131065 FBN3 chr3 152554179 152554351 P2RY1 chrX 48887845 48887953 TFE3chr11 89774235 89774445 TRIM49C chr6 74227784 74227974 EEF1A1 chr632551969 32552138 HLA-DRB1 chr10 105484023 105484122 SH3PXD2A chr227717413 27717546 FNDC4 chr1 203154334 203154457 CHI3L1 chr12 970255970354 WNK1 chr19 11577555 11577654 ELAVL3 chr10 123258008 123258119FGFR2 chr5 56183204 56183347 MAP3K1 chr2 9989495 9989594 TAF1B chr1712011106 12011226 MAP2K4 chr7 100245061 100245160 ACTL6B chr12 44795774479740 FGF23 chrX 49040087 49040186 PRICKLE3 chr1 7890009 7890108 PER3chr19 55325382 55325489 KIR2DL4 chr6 26406256 26406425 BTN3A1 chr108105955 8106101 GATA3 chr12 112600810 112600909 HECTD4 chr6 3248973532489834 HLA-DRB5 chr17 4875688 4875787 CAMTA2 chr12 6702256 6702394CHD4 chr16 67645853 67646024 CTCF chr2 70905836 70906015 ADD2 chr1940383694 40383994 FCGBP chr6 26123758 26124001 HIST1H2BC chr9 7878996178790208 PCSK5 chr8 12285063 12285251 FAM86B2 chr19 54745495 54745683LILRA6 chr12 115120615 115120804 TBX3 chr7 150884014 150884269 ASB10chr1 12979941 12980233 PRAMEF7 chr14 38060746 38061532 FOXA1 chr632549361 32549564 HLA-DRB1 chr16 15696435 15696534 KIAA0430 chrX154133100 154133269 F8 chr11 2434050 2434149 TRPM5 chr20 6144456761444666 OGFR chr17 12016549 12016677 MAP2K4 chr5 67591053 67591152PIK3R1 chr15 22077530 22077704 POTEB chr7 100773700 100773848 SERPINE1chrX 119292961 119293092 RHOXF2 chrX 31525397 31525570 DMD chr1 2705772627057935 ARID1A chr7 37960219 37960318 EPDR1 chr1 230979428 230979527C1orf198 chr19 55399501 55399643 FCAR chr19 48945465 48945576 GRIN2Dchr21 36171597 36171759 RUNX1 chr21 36206715 36206875 RUNX1 chr1615802633 15802732 MYH11 chr3 49412866 49413022 RHOA chr1 170521253170521403 GORAB chr17 12032455 12032604 MAP2K4 chr11 64457862 64457961NRXN2 chr13 27664011 27664110 USP12 chr19 41596308 41596469 CYP2A13chr17 61619619 61619778 KCNH6 chr2 27356067 27356187 PREB chr3 178917469178917568 PIK3CA chr2 36994265 36994429 VIT chr16 1291453 1291623 TPSAB1chr16 68857310 68857497 CDH1 chr12 12870830 12871245 CDKN1B chr2020269279 20269470 C20orf26 chr19 40376674 40377035 FCGBP chr22 3566129735661545 HMGXB4 chr17 77768442 77768692 CBX8 chr9 136131208 136131417ABO chr11 46563793 46564008 AMBRA1 chr20 48604441 48604540 SNAI1 chr731735082 31735235 PPP1R17 chr3 52402776 52402875 DNAH1 chr7 150718274150718416 ATG9B chr11 66335454 66335553 CTSF chr1 245582880 245583047KIF26B chr16 68846037 68846166 CDH1 chr16 75690147 75690321 TERF2IP chr625600807 25600906 LRRC16A chr6 42897308 42897459 CNPY3 chr1 145323629145323728 NBPF10 chr20 20033035 20033189 CRNKL1 chr4 126328145 126328244FAT4 chr9 377062 377200 DOCK8 chr1 242253179 242253347 PLD5 chr4144545278 144545443 FREM3 chr6 26250514 26250662 HIST1H3F chr10 9605814496058294 PLCE1 chr19 6183141 6183251 ACSBG2 chr1 159683726 159683856 CRPchr19 4292605 4292733 TMIGD2 chr11 49204697 49204796 FOLH1 chrX 1882201218822167 PPEF1 chr1 33099237 33099336 ZBTB8OS chr19 1084236 1084345HMHA1 chr12 133198140 133198306 P2RX2 chr8 70981417 70981516 PRDM14 chrX114082632 114082731 HTR2C chr12 130827125 130827224 PIWIL1 chrX 4907185149071973 CACNA1F chr2 98165868 98165967 ANKRD36B chr6 25776821 25776982SLC17A4 chr12 130648737 130648882 FZD10 chr20 58547016 58547178 CDH26chr16 57760038 57760137 CCDC135 chr6 102124572 102124671 GRIK2 chr1914748955 14749064 EMR3 chr10 49388900 49389051 FRMPD2 chr14 2403548624035628 AP1G2 chrX 11162125 11162224 ARHGAP6 chr11 121000378 121000477TECTA chr19 16841999 16842098 NWD1 chr13 33638165 33638278 KL chr2246712077 46712236 GTSE1 chrX 119005874 119005976 NDUFA1 chr17 7349097673491115 KIAA0195 chr13 36886455 36886614 SPG20 chr20 45867566 45867733ZMYND8 chr9 2643252 2643404 VLDLR chr1 29189399 29189498 OPRD1 chr5175782615 175782744 KIAA1191 chr7 107823275 107823374 NRCAM chr2226868797 26868905 HPS4 chr1 54417811 54417910 LRRC42 chr3 4126869841268843 CTNNB1 chr2 241631330 241631462 AQP12A chr10 89653774 89653873PTEN chr6 167790033 167790192 TCP10 chr3 86010665 86010764 CADM2 chr285051080 85051179 TRABD2A chr6 48035985 48036157 PTCHD4 chr19 4039245440392803 FCGBP chr11 57569454 57569631 CTNND1 chr21 41719609 41719831DSCAM chrX 91873396 91873743 PCDH11X chr3 130116501 130116761 COL6A5chr1 154841539 154842331 KCNN3 chr1 12939484 12939864 PRAMEF4 chr9105767464 105767685 CYLC2 chr7 65705508 65705729 TPST1 chr7 151945139151945695 MLL3 chr17 58260549 58260772 USP32 chr3 121825055 121825335CD86 chr15 23684996 23686765 GOLGA6L2 chr3 105421039 105421268 CBLB chr984202595 84202743 TLE1 chr4 151223776 151223944 LRBA chr18 3026013130260290 KLHL14 chr19 52521620 52521747 ZNF614 chr19 40741890 40741989AKT2 chr20 45188680 45188779 SLC13A3 chr1 115222989 115223088 AMPD1 chr4151829485 151829619 LRBA chr6 79577309 79577408 IRAK1BP1 chr6 3761991537620077 MDGA1 chr2 197709223 197709322 PGAP1 chr8 28595035 28595180EXTL3 chr5 195143 195277 LRRC14B chr5 56161166 56161283 MAP3K1 chr1629821397 29821552 MAZ chr2 138000026 138000142 THSD7B chr1 171154852171154984 FMO2 chr17 10312638 10312804 MYH8 chr7 103051866 103052033SLC26A5 chrX 107396857 107396956 ATG4A chr22 19127367 19127537 DGCR14chr12 99074054 99074180 APAF1 chr7 106526579 106526737 PIK3CG chr7128658008 128658107 TNPO3 chr16 20575995 20576156 ACSM2B chr12 45544154554554 FGF6 chr19 36584932 36585066 WDR62 chr2 25978885 25978984 ASXL2chr1 234565965 234566064 TARBP1 chr1 17266447 17266546 CROCC chr126091076 6091175 VWF chr13 31903637 31903805 B3GALTL chr19 4834291148343010 CRX chr19 41743890 41743989 AXL chr5 67589536 67589662 PIK3R1chr22 17978441 17978581 CECR2 chrX 128631870 128632008 SMARCA1 chr127343047 7343152 PEX5 chr4 17585105 17585265 LAP3 chr20 3209483 3209657SLC4A11 chr7 100187263 100187416 FBXO24 chr16 29994054 29994222 TAOK2chrX 29935580 29935713 IL1RAPL1 chr20 42965923 42966022 R3HDML chr227601435 27601551 ZNF513 chr1 155265227 155265359 PKLR chr6 3263427532634384 HLA-DQB1 chr6 27805734 27806085 HIST1H2AK chr7 2163951121639694 DNAH11 chr21 37444932 37445118 CBR1 chr5 56177409 56178674MAP3K1 chr17 63221254 63221455 RGS9 chrX 8434191 8434393 VCX3B chr289277989 89278194 IGKV3-7 chrX 131762516 131762943 HS6ST2 chr12 5738941157389645 GPR182 chr2 130831830 130833037 POTEF chr2 132021022 132022032POTEE chr17 80159566 80159706 CCDC57 chr4 154191481 154191580 TRIM2chr15 91424577 91424680 FURIN chr9 34655582 34655681 IL11RA chr916552595 16552754 BNC2 chr3 53217129 53217228 PRKCD chr17 6157081161570910 ACE chr17 29483000 29483144 NF1 chr6 74072452 74072621 KHDC3Lchr14 24040434 24040654 JPH4 chr2 27248450 27248605 MAPRE3 chr1667100584 67100701 CBFB chr16 28506484 28509154 APOBR chr2 129025859129026228 HS6ST1 chr13 39587206 39587683 PROSER1 chr2 90078036 90078271IGKV3D-20 chr2 234680925 234681080 UGT1A4 chrX 70472830 70472964 ZMYM3chr1 68624805 68624930 WLS chr2 25967089 25967232 ASXL2 chr16 6884722468847371 CDH1 chrX 21450737 21450903 CNKSR2 chr12 122242643 122242817SETD1B chr19 51628888 51629053 SIGLEC9 chr22 37962637 37962797 CDC42EP1chr17 56056512 56056674 VEZF1 chr4 1388323 1389290 CRIPAK chr2 2123608021236261 APOB chrX 38144854 38146403 RPGR chrX 119004943 119005377RNF113A chr12 11244066 11244726 TAS2R43 chr5 26881368 26881733 CDH9 chr719184660 19184944 FERD3L chr5 56170879 56171089 MAP3K1 chrX 151899871151900721 MAGEA12 chr1 240370193 240371705 FMN2 chrX 99661924 99663462PCDH19 chr6 26043522 26043738 HIST1H2BB chr19 55450429 55451645 NLRP7chr12 125396334 125398031 UBC chr14 69256737 69257170 ZFP36L1 chr1474042018 74042190 ACOT2 chr9 69421915 69422014 ANKRD20A4 chr19 4991300949913132 CCDC155 chr5 78181482 78181581 ARSB chr7 45120238 45120361NACAD chrX 65819448 65819550 EDA2R chr1 8421429 8421528 RERE chr1543701211 43701310 TP53BP1 chr5 56155571 56155727 MAP3K1 chr6 2777620827776370 HIST1H2AI chr1 233136089 233136234 PCNXL2 chr17 5638638256386481 BZRAP1 chr17 53398052 53398151 HLF chrX 12627839 12628000FRMPD4 chr12 7527906 7528005 CD163L1 chr6 86332253 86332355 SYNCRIP chr633263911 33264010 RGL2 chr13 37569561 37569733 ALG5 chr1 6724293567243067 TCTEX1D1 chr10 72604229 72604395 SGPL1 chr16 68867202 68867354CDH1 chr10 99379269 99379410 MORN4 chr1 150917574 150917673 SETDB1 chr5139192984 139193083 PSD2 chr20 31041506 31041605 C20orf112 chr16 48620864862249 GLYR1 chr6 32551966 32552065 HLA-DRB1 chr6 28093419 28093518ZSCAN16 chr1 26608848 26608947 UBXN11 chr9 19096669 19096768 HAUS6 chr7128491508 128491682 FLNC chr12 25398207 25398318 KRAS chr1 247320232247320337 ZNF124 chr13 25021153 25021324 PARP4 chr2 159481539 159481710PKP4 chr3 178922283 178922382 PIK3CA chr1 170695376 170695531 PRRX1 chr2169996960 169997059 LRP2 chr10 89624216 89624315 PTEN chr10 3065380730653906 MTPAP chr9 95277169 95277330 ECM2 chr2 166245337 166246185SCN2A chr6 26056018 26056591 HIST1H1C chr16 26147093 26147546 HS3ST4chr6 13977497 13978084 RNF182 chr6 27115003 27115268 HIST1H2AH chrX34960975 34962645 FAM47B chr6 26216531 26216717 HIST1H2BG chr12 5269984252700028 KRT86 chr14 60938268 60938455 C14orf39 chr2 234652186 234652467DNAJB3 chrX 134427657 134427940 ZNF75D chr7 86415633 86415917 GRM3 chr11118770650 118770853 BCL9L chr1 190067487 190068154 FAM5C chr17 1633533416335540 TRPV2 chr9 27948860 27950484 LINGO2 chr6 29454651 29455669MAS1L chr17 40714738 40715280 COASY chr10 46998994 47000218 GPRIN2 chr2240982115 240982328 PRR21 chr15 20739674 20740539 GOLGA6L6 chr1149858553 149858867 HIST2H2AC chr12 46245782 46246211 ARID2 chr2054961338 54961557 AURKA chr2 165550902 165551967 COBLL1 chr19 3810256538104062 ZNF540 chr12 81111100 81111321 MYF5 chrX 48418496 48419227TBC1D25 chr1 149857850 149858181 HIST2H2BE chr6 31238879 31239110 HLA-Cchr15 83926260 83926491 BNC1 chr3 130095170 130095628 COL6A5 chr1739637092 39637327 KRT35 chr7 72412439 72414041 POM121 chr16 6764473567645508 CTCF chr1 235345090 235345864 ARID4B chr17 262972 263748C17orf97 chrX 149638545 149639506 MAMLD1 chr6 33169118 33169361 SLC39A7chr21 47664836 47665081 MCM3AP chr6 167754301 167754657 TTLL2 chr1176863700 176863947 ASTN1 chr13 92345526 92346013 GPC5 chr21 4057075040571559 BRWD1 chr9 138395416 138395776 MRPS2 chrX 148037135 148037947AFF2 chrX 111155633 111155994 TRPC5 chr19 37879509 37880727 ZNF527 chr1146419037 46419290 AMBRA1 chr12 125834061 125834885 TMEM132B chr1158919837 58920661 FAM111A chr7 26224165 26225183 NFE2L3 chr1 156843433156843687 NTRK1 chr2 80529541 80530775 LRRTM1 chrX 30260295 30261122MAGEB4 chr6 114378676 114379176 HS3ST5 chr12 11546089 11546743 PRB2 chr2160035346 160035601 TANC1 chr15 86807529 86808033 AGBL1 chr17 2131869721319944 KCNJ12 chr1 176564440 176564697 PAPPA2 chr3 56667119 56667625FAM208A chrX 48681326 48681991 HDAC6 chr19 36673393 36674068 ZNF565chr12 5020628 5021917 KCNA1 chr17 38643341 38643607 TNS4 chr9 121929580121930447 DBC1 chr20 50768781 50769650 ZFP64 chr2 145147115 145147384ZEB2 chr14 107012976 107013246 IGHV3-49 chrX 99551395 99551785 PCDH19chr15 33954548 33954820 RYR3 chr5 90086904 90087074 GPR98 chr11 6109308161093180 DDB1 chrX 10176284 10176394 CLCN4 chr1 151261057 151261156ZNF687 chr16 61689465 61689593 CDH8 chr1 78478781 78478899 DNAJB4 chr7132937850 132937949 EXOC4 chr17 16029395 16029520 NCOR1 chr19 5467785154678003 MBOAT7 chr16 56782198 56782316 NUP93 chr1 181726104 181726203CACNA1E chr1 186014822 186014958 HMCN1 chr14 74967586 74967732 LTBP2chr19 55598889 55598988 EPS8L1 chr16 22337427 22337526 POLR3E chr3180685863 180686032 FXR1 chrX 51238892 51238991 NUDT11 chr21 4171006141710186 DSCAM chr6 161071369 161071529 LPA chr5 140960310 140960450DIAPH1 chr3 51417547 51417646 DOCK3 chr1 200973893 200974061 KIF21Bchr21 28315698 28315866 ADAMTS5 chr10 105207115 105207214 CALHM2 chr1028905130 28905247 WAC chr20 1961171 1961313 PDYN chr19 36303266 36303419PRODH2 chr11 60183853 60184022 MS4A14 chr10 100503639 100503813 HPSE2chr17 18205577 18205748 TOP3A chr2 66798407 66798506 MEIS1 chr1169578746 169578897 SELF chr20 47601265 47601377 ARFGEF2 chr1 3374591133746010 ZNF362 chrX 2779578 2779693 GYG2 chr19 18652622 18652721 FKBP8chr17 8158786 8158885 PFAS chr12 120634575 120634737 RPLP0 chr9139357442 139357555 SEC16A chr2 233785115 233785250 NGEF chr4 190874201190874300 FRG1 chr17 8224159 8224311 ARHGEF15 chr11 63670086 63670185MARK2 chr4 147724635 147724766 TTC29 chrX 135592240 135592378 HTATSF1chr22 31487660 31487833 SMTN chr19 11287290 11287450 KANK2 chr6 2840376228403873 ZSCAN23 chr19 33470934 33471065 RHPN2 chr2 204820384 204820527ICOS chr12 72338080 72338179 TPH2 chr7 100284271 100284442 GIGYF1 chr162134563 2134662 TSC2 chr17 74943920 74944019 MGAT5B chr7 2714801127148110 HOXA3 chr1 6257711 6257816 RPL22 chr8 2975909 2976008 CSMD1chr5 56167736 56167858 MAP3K1 chr5 56168458 56168557 MAP3K1 chr556174806 56174928 MAP3K1 chr5 56181758 56181890 MAP3K1 chr19 1337037713370515 CACNA1A chr7 142124195 142124360 TRBV6-8 chr2 90139365 90139530IGKV1D-16 chr1 13474848 13474984 PRAMEF18 chr4 46967043 46967142 GABRA4chr11 116827663 116827780 SIK3 chr17 28890297 28890396 TBC1D29 chr280773031 80773188 CTNNA2 chr15 41099875 41100006 ZFYVE19 chr7 9177984391780009 LRRD1 chr1 155290250 155290349 FDPS chr9 3346665 3346764 RFX3chr9 97873812 97873911 FANCC chr11 49053333 49053432 TRIM49B chr143296635 43296768 ERMAP chr5 32233878 32234040 MTMR12 chr19 1487646914876616 EMR2 chr9 2729501 2729632 KCNV2 chr1 120484228 120484368 NOTCH2chr3 108723918 108724024 MORC1 chr12 41419004 41419118 CNTN1 chr12115115373 115115472 TBX3 chr8 48887307 48887473 MCM4 chr22 1912181119121973 DGCR14 chr11 68305213 68305349 PPP6R3 chr1 176926813 176926964ASTN1 chr21 40834342 40834441 SH3BGR chr12 130184678 130184777 TMEM132Dchr1 19464531 19464665 UBR4 chr6 127648146 127648289 ECHDC1 chr1160279965 160280064 COPA chr12 28114824 28114930 PTHLH chr11 119210188119210296 C1QTNF5 chr12 130833861 130833960 PIWIL1 chrX 1053521410535386 MID1 chr12 21168622 21168721 SLCO1B7 chr5 154173388 154173559LARP1 chr12 6344636 6344735 CD9 chr17 61557129 61557273 ACE chr7130023231 130023333 CPA1 chr6 39507793 39507967 KIF6 chr2 198267360198267494 SF3B1 chr17 11597184 11597315 DNAH9 chr2 74763874 74763973LOXL3 chr11 62381006 62381105 ROM1 chr19 33098607 33098732 ANKRD27 chr116452419 6452518 HPX chr12 54645831 54645967 CBX5 chr1 149871794149871947 BOLA1 chr12 1740510 1740609 WNT5B chr9 113637768 113637891LPAR1 chr7 128852210 128852309 SMO chr17 73774671 73774804 H3F3B chr848811029 48811129 PRKDC chr12 111923516 111923669 ATXN2 chr12 130648737130648882 FZD10 chr17 67190035 67190134 ABCA10 chr12 18852727 18852884PLCZ1 chr17 60023828 60023961 MED13 chr1 26885297 26885428 RPS6KA1 chr191783032 1783131 ATP8B3 chr12 111321893 111322028 CCDC63 chr2 1537472015374819 NBAS chr2 220494023 220494122 SLC4A3 chr1 2303919 2304030 MORN1chr1 16891301 16891413 NBPF1 chrX 114242494 114242639 IL13RA2 chr1212911795 212911894 NSL1 chr20 58559693 58559860 CDH26 chrX 122528818122528980 GRIA3 chr7 97833266 97833437 LMTK2 chr12 66531836 66531938TMBIM4 chr22 41752346 41752480 ZC3H7B chr11 46917434 46917569 LRP4 chr1151509205 151509369 CGN chr7 143055976 143056091 FAM131B chr1 4528814445288243 PTCH2 chr10 94653105 94653277 EXOC6 chr11 74880240 74880339SLCO2B1 chr1 153043147 153043246 SPRR2B chr18 66354903 66355002 TMX3chr17 37868180 37868300 ERBB2 chr3 176769248 176769347 TBL1XR1 chr1955107146 55107252 LILRA1 chrX 117570664 117570787 WDR44 chr8 8067744980677555 HEY1 chr5 67589148 67589270 PIK3R1 chr1 160769621 160769720 LY9chr12 100660697 100660854 DEPDC4 chr17 74623496 74623665 ST6GALNAC1 chr6135511265 135511400 MYB chr6 44224078 44224233 SLC35B2 chr20 3053428930534388 PDRG1 chr17 66871754 66871874 ABCA8 chr8 103284778 103284938UBR5 chr17 59557505 59557604 TBX4 chrX 47500669 47500827 ELK1 chr1762892221 62892320 LRRC37A3 chr19 51323154 51323291 KLK1 chr15 7195287071952969 THSD4 chr1 116280844 116280956 CASQ2 chr1 113616169 113616268LRIG2 chr19 40368617 40368716 FCGBP chr20 18429620 18429719 DZANK1 chr331725366 31725492 OSBPL10 chr3 31871578 31871702 OSBPL10 chr3 101572096101572247 NFKBIZ chr9 15489983 15490122 PSIP1 chr3 115395121 115395258GAP43 chr12 20806921 20807085 PDE3A chr1 107691295 107691450 NTNG1 chr11126136657 126136817 SRPR chr16 70595532 70595687 SF3B3 chr6 49438554943954 CDYL chr16 29472706 29472854 SULT1A4 chr4 71500187 71500286 ENAMchr4 100521721 100521890 MTTP chr11 289843 289955 ATHL1 chr16 2891357728913676 ATP2A1 chr15 38614441 38614610 SPRED1 chr1 16265790 16265922SPEN chrX 39922947 39923046 BCOR chr1 12405430 12405566 VPS13D chr1253041956 53042121 KRT2 chr2 108479164 108479276 RGPD4 chr6 3510852335108661 TCP11 chr12 108603943 108604056 WSCD2 chr8 104709325 104709424RIMS2 chr5 129243892 129243991 CHSY3 chr13 24860362 24860472 SPATA13chrX 48672846 48672973 HDAC6 chr5 37169183 37169282 C5orf42 chrX74296356 74296489 ABCB7 chr17 26101296 26101431 NOS2 chr10 9053785590537957 LIPN chr2 198363398 198363572 HSPD1 chr17 73100131 73100285SLC16A5 chr20 25755848 25755947 FAM182B chr15 25966885 25966984 ATP10Achr9 12702270 12702442 TYRP1 chr9 35616075 35616246 CD72 chr1 4413485444134953 KDM4A chr2 1926144 1926291 MYT1L chr12 91371888 91371987 EPYCchr15 43668295 43668424 TUBGCP4 chr3 151107766 151107923 MED12L chr1213529164 13529263 C12orf36 chr19 47492800 47492932 ARHGAP35 chrX134185955 134186116 FAM127B chr5 137289941 137290040 FAM13B chr2061907831 61908003 ARFGAP1 chr5 14358286 14358456 TRIO chr4 18381551838299 LETM1 chr2 99634662 99634812 TSGA10 chr10 43597800 43597900 RETchr3 148871280 148871435 HPS3 chrX 114524321 114524420 LUZP4 chr1257498952 57499095 STAT6 chr3 112710096 112710195 GTPBP8 chr3 178937358178937523 PIK3CA chr1 149939345 149939444 OTUD7B chr6 76640678 76640798IMPG1 chr2 71839770 71839936 DYSF chr15 75111492 75111633 LMAN1L chr1170695408 170695542 PRRX1 chr7 120496734 120496833 TSPAN12 chr1 5176786351767962 TTC39A chr15 101447325 101447483 ALDH1A3 chr1 29609284 29609432PTPRU chr15 28769084 28769183 GOLGA8G chr14 64580037 64580136 SYNE2 chr626217292 26217391 HIST1H2AE chr19 49982165 49982304 F1T3LG chrX130409472 130409571 IGSF1 chr1 11317096 11317206 MTOR chr1 206611313206611448 SRGAP2 chr17 41931250 41931349 CD300LG chr19 10781687 10781835ILF3 chr6 131925317 131925460 MED23 chr3 184035081 184035180 EIF4G1 chrX85403969 85404068 DACH2 chr1 215408279 215408415 KCNK2 chr15 8352339583523552 HOMER2 chr18 14850212 14850381 ANKRD30B chr4 173961083173961251 GALNTL6 chr9 123888015 123888114 CNTRL chr1 175067599175067698 TNN chr7 73279501 73279649 WBSCR28 chr7 100170019 100170193SAP25 chr12 89818981 89819119 POC1B chr8 53038606 53038705 ST18 chr1367205357 67205532 PCDH9 chr16 1129032 1129207 SSTR5 chr20 5040080950400984 SALL4 chr12 69656160 69656335 CPSF6 chr2 43452473 43452871ZFP36L2 chr17 66246372 66246549 AMZ2 chr12 56478825 56479002 ERBB3 chr1715964870 15965148 NCOR1 chr12 76424349 76425063 PHLDA1 chr20 27748802775058 CPXM1 chr12 112460033 112460211 ERP29 chrX 107018375 107018553TSC22D3 chrX 23397728 23398007 PTCHD1 chr16 28884770 28885050 SH2B1chr15 42052535 42052714 MGA chr19 12154700 12154982 ZNF878 chr6 9066021090661582 BACH2 chr22 17450867 17451048 GAB4 chr3 36484913 36485095 STACchr21 40794924 40795106 LCA5L chr14 52186773 52187058 FRMD6 chr1421215830 21216115 EDDM3A chr1 197479778 197480064 DENND1B chr6 7589298375893167 COL12A1 chr1 240656325 240656741 GREM2 chr19 53793013 53793430BIRC8 chr3 38991613 38991798 SCN11A chr17 16326826 16327011 TRPV2 chrX17750085 17750270 NHS chr19 814467 814653 LPPR3 chrX 118284279 118284465KIAA1210 chr8 88885139 88886088 DCAF4L2 chrX 125685469 125686221DCAF12L1 chr22 22730662 22730850 IGLV5-45 chr11 125325767 125325955 FEZ1chr16 3293330 3293518 MEFV chr2 202149564 202149752 CASP8 chr5 153149726153149915 GRIA1 chrX 147743619 147744201 AFF2 chr4 16504297 16504487LDB2 chr20 41419913 41420104 PTPRT chr4 122853548 122853848 TRPC3 chr1951165615 51165807 SHANK1 chr7 100349542 100350751 ZAN chr1 114225697114226132 MAGI3 chr17 68171418 68172398 KCNJ2 chr11 120352006 120352199ARHGEF12 chr20 31671212 31671649 BPIFB4 chr4 139980482 139980676 ELF2chr16 62055070 62055265 CDH8 chr6 26188993 26189188 HIST1H4D chr2209025575 209025770 CRYGA chr14 95053767 95053963 SERPINA5 chr5140589609 140590840 PCDHB12 chr1 120458146 120458943 NOTCH2 chr2166201097 166201297 SCN2A chr12 10978186 10978500 TAS2R10 chr8 109796470109797276 TMEM74 chr6 11190311 11191332 NEDD9 chr2 56144945 56145147EFEMP1 chr1 160920835 160921038 ITLN2 chr5 118835029 118835233 HSD17B4chr3 3189136 3189340 TRNT1 chr2 132288158 132288363 CCDC74A chr348694416 48694739 CELSR3 chr12 53775932 53776139 SP1 chr17 7679965576799862 USP36 chr12 5153646 5155078 KCNA5 chr3 196434455 196434663CEP19 chr7 77789381 77789589 MAGI2 chr7 37780042 37780878 GPR141 chr6154412131 154412458 OPRM1 chr19 52537524 52538587 ZNF432 chr16 396353396826 AXIN1 chr14 72139080 72139290 SIPA1L1 chr16 9857874 9858522GRIN2A chr6 26199107 26199319 HIST1H2AD chr2 90025216 90025428 IGKV2D-26chr3 129389466 129389678 TMCC1 chr20 23016242 23017093 SSTR4 chr189448781 89449435 RBMXL1 chr20 896597 896810 ANGPT4 chr17 3964566939645882 KRT36 chr16 15702157 15702370 KIAA0430 chr21 38884439 38884773DYRK1A chr7 128119301 128119515 METTL2B chr20 5903618 5904478 CHGB chr1164627437 64627774 EHD1 chr19 58370284 58371379 ZNF587 chr1 1943914419439360 UBR4 chr5 140580561 140581432 PCDHB11 chr19 51021545 51022418LRRC4B chr16 22926373 22926864 HS3ST2 chr14 95921719 95921937 SYNE3chr17 46629395 46629738 HOXB3 chr9 5300143 5300363 RLN2 chr13 3604938436050060 MAB21L1 chr14 94087992 94089111 UNC79 chr1 248039225 248039570TRIM58 chr8 124195350 124195571 FAM83A chr1 28920327 28920548 RAB42chr12 129558460 129559468 TMEM132D chrX 30872291 30873432 TAB3 chrX5811008 5811361 NLGN4X chr15 32929243 32929936 ARHGAP11A chr6 7817223278172742 HTR1B chr3 121206757 121207555 POLQ chrX 78216026 78216941P2RY10 chr12 7045007 7046169 ATN1 chr6 26271218 26271576 HIST1H3G chr198807879 8808586 ACTL9 chr1 206224465 206224827 AVPR1B chr2 182542798182543322 NEUROD1 chrX 17768049 17768340 SCML1 chr6 17637545 17637837NUP153 chr21 39086564 39087179 KCNJ6 chr14 106173557 106173791 IGHA1chr17 38253388 38253622 NR1D1 chr11 96117311 96117840 CCDC82 chr1216430302 16430537 SLC15A5 chr1 214170479 214171545 PROX1 chr19 1551196915512206 AKAP8L chr2 131414336 131414574 POTEJ chr12 71977910 71978453LGR5 chr7 82763886 82764264 PCLO chr5 76028287 76029029 F2R chr6155450748 155451384 TIAM2 chr14 24845638 24845883 NFATC4 chr15 5390784153908086 WDR72 chr13 108518035 108518788 FAM155A chr12 47629615 47630076PCED1B chr19 51645627 51646011 SIGLEC7 chrX 77244949 77245411 ATP7A chr7126173022 126173578 GRM8 chr19 19906155 19906464 ZNF506 chrX 102931121102931368 MORF4L2 chr4 25005572 25005819 LGI2 chr2 227872736 227872983COL4A4 chrX 75003458 75004574 MAGEE2 chr7 108204867 108205255 THAP5chr19 52217058 52217307 HAS1 chr9 139390622 139390871 NOTCH1 chr1952888047 52888439 ZNF880 chr1 237947086 237948219 RYR2 chrX 3026863830269645 MAGEB1 chrX 64721695 64722832 ZC3H12B chr1 221912293 221913068DUSP10 chr7 39503849 39504102 POU6F2 chr19 51273961 51274852 GPR32 chrX12735732 12736884 FRMPD4 chrX 152225667 152226243 PNMA3 chr3 8803997488040230 HTR1F chr8 56435861 56436761 XKR4 chrX 155003546 155004222SPRY3 chr17 26861800 26862057 FOXN1 chrX 68382801 68383058 PJA1 chr5137680988 137681245 FAM53C chr1 12942943 12943201 PRAMEF4 chr1 231344719231344977 TRIM67 chr2 99013186 99013590 CNGA3 chr1 171251125 171251384FMO1 chr7 96635419 96635681 DLX6 chr6 139487509 139487771 HECA chr788423579 88424170 C7orf62 chr7 99956434 99956697 PILRB chr2 133402800133402997 GPR39 chr1 183511386 183511584 SMG7 chr12 56397549 56397814SUOX chr19 35232114 35232613 ZNF181 chr7 150171134 150171635 GIMAP8 chr775028333 75028600 TRIM73 chr1 25572974 25573241 C1orf63 chr22 3990983039910166 SMCR7L chr10 91198587 91198856 SLC16A12 chr20 61542180 61542889DIDO1 chr20 50701236 50701661 ZFP64 chr3 13860452 13860792 WNT7A chr9111625372 111625798 ACTL7A chr19 7676699 7677125 CAMSAP3 chrX 103080349103080690 RAB9B chrX 135593185 135594143 HTATSF1 chrX 112058601112058874 AMOT chr14 20019841 20020114 POTEM chr2 239164300 239164505PER2 chr6 153043014 153043357 MYCT1 chr11 209436 209711 RIC8A chr251254719 51255150 NRXN1 chrX 118971733 118971941 UPF3B

TABLE 7 Chromosome Start (bp) End (bp) Gene chr12 25398207 25398318 KRASchr6 170870990 170871089 TBP chr7 128587317 128587416 IRF5 chr9 9643889296439020 PHF2 chr11 117789286 117789385 TMPRSS13 chr17 7577018 7577155TP53 chr17 7578369 7578551 TP53 chr17 7577498 7577608 TP53 chr1756833438 56833614 PPM1E chr3 178935997 178936122 PIK3CA chr17 75781767578289 TP53 chr12 132547047 132547146 EP400 chr7 140453074 140453193BRAF chr9 140918128 140918227 CACNA1B chr21 46924329 46924470 COL18A1chr18 48591824 48591932 SMAD4 chr5 112116486 112116600 APC chr1154841790 154842346 KCNN3 chr19 58549260 58549532 ZSCAN1 chr17 7235040172350579 KIF19 chr19 39330909 39331008 HNRNPL chr22 29885015 29886640NEFH chr3 41266058 41266157 CTNNB1 chr19 54754649 54754796 LILRB5 chr21271163 1271319 SNTG2 chr12 133219467 133219580 POLE chr1 2710007027100208 ARID1A chr5 112173345 112179738 APC chr9 12775812 12775911LURAP1L chr19 56599373 56599472 ZNF787 chr13 46170598 46171110 FAM194Bchr1 29138925 29139024 OPRD1 chr10 17659090 17659189 PTPLA chr2 1181004311810142 NTSR2 chr20 32664822 32664921 RALY chr12 53068987 53069344 KRT1chr14 93154359 93154541 RIN3 chr19 17932137 17932290 INSL3 chr6 1632665716328230 ATXN1 chr20 46279801 46279900 NCOA3 chr1 85039985 85040084 CTBSchr19 1064981 1065080 ABCA7 chr1 21044068 21044167 KIF17 chr2 187558955187559054 FAM171B chr17 6899436 6899571 ALOX12 chr7 130418475 130418574KLF14 chr9 124855210 124855332 TTLL11 chr7 1586652 1586812 TMEM184A chr8143808950 143809194 THEM6 chr4 88535232 88537514 DSPP chr1 228504471228504671 OBSCN chr11 320605 320806 IFITM3 chr20 44420643 44420748DNTTIP1 chr17 74381511 74381610 SPHK1 chr19 2226674 2226773 DOT1L chr1566274640 66274739 MEGF11 chr16 84224917 84225016 ADAD2 chr16 3115413931154238 PRSS36 chr7 6566298 6566397 GRID2IP chr3 121351263 121351362HCLS1 chr1 200880977 200881173 C1orf106 chr3 178916650 178916958 PIK3CAchr2 98611944 98612043 TMEM131 chr19 17393464 17393570 ANKLE1 chr5112128134 112128233 APC chr20 60887455 60887588 LAMA5 chr16 602312602512 SOLH chr1 152487916 152488147 CRCT1 chr8 145001587 145001785 PLECchr13 28367011 28367110 GSX1 chr12 124824644 124824743 NCOR2 chr1176751523 76751622 B3GNT6 chr17 40706742 40706907 HSD17B1 chr18 5688749756887636 GRP chr3 178951963 178952087 PIK3CA chr10 104159146 104159245NFKB2 chr15 78441709 78441808 IDH3A chr2 42275814 42275913 PKDCC chr1195825253 95826577 MAML2 chr19 56041254 56041623 SBK2 chrX 6676503166766111 AR chr19 58384471 58386127 ZNF814 chr1 26608827 26609017 UBXN11chr8 144775907 144776528 ZNF707 chr16 24788422 24788646 TNRC6A chr192732780 2733356 SLC39A3 chr17 36508384 36508582 SOCS7 chr3 5141754751417646 DOCK3 chr19 15284978 15285087 NOTCH3 chr8 120220760 120220859MAL2 chr15 60690041 60690140 ANXA2 chr16 15122734 15122889 PDXDC1 chr1161658750 61658849 FADS3 chr19 4499590 4499689 HDGFRP2 chr19 1739286517393018 ANKLE1 chr16 3304157 3304672 MEFV chr20 43348541 43348751 WISP2chr5 140214076 140216118 PCDHA7 chr13 111367954 111368317 ING1 chr1332885653 32885906 ZAR1L chr6 44243153 44243560 TMEM151B chr17 46930534693343 GLTPD2 chr20 3732264 3732634 HSPA12B chr17 39684144 39684438KRT19 chr19 6737467 6737587 GPR108 chr19 49611231 49611330 SNRNP70 chr12124829233 124829400 NCOR2 chr4 153249359 153249520 FBXW7 chr19 1744891117449010 GTPBP3 chr8 145742795 145742894 RECQL4 chr20 590521 590620TCF15 chr12 122242643 122242817 SETD1B chr7 150037524 150037698 RARRES2chr1 227922917 227923082 JMJD4 chr7 44924577 44924676 PURB chr10105110691 105110790 PCGF6 chr19 45867243 45867377 ERCC2 chr12 5761920857619447 NXPH4 chr20 37377138 37377455 ACTR5 chr6 29910532 29910744HLA-A chr2 239049467 239050143 KLHL30 chr9 25677697 25677954 TUSC1 chr1321562370 21563346 LATS2 chr2 39187172 39187520 ARHGEF33 chr18 31887793188977 MYOM1 chr22 20780023 20780297 SCARF2 chr6 53516875 53517036KLHL31 chr19 36002347 36002446 DMKN chr2 36825104 36825203 FEZ2 chr1153907243 153907342 DENND4B chr10 29760066 29760172 SVIL chr22 2909169729091861 CHEK2 chr3 150421508 150421607 FAM194A chr20 44520189 44520288CTSA chr12 113376370 113376469 OAS3 chr12 122359394 122359516 WDR66chr19 47768029 47768203 CCDC9 chr19 17337506 17337605 OCEL1 chr10102988328 102988427 LBX1 chr2 148683599 148683730 ACVR2A chr11 1703566017035759 PLEKHA7 chrX 295101 295252 PPP2R3B chr17 17119693 17119817 FLCNchr5 112162804 112162944 APC chr8 8860573 8860681 ERI1 chr10 8599698485997269 LRIT1 chr7 2577780 2578372 BRAT1 chr6 29911106 29911320 HLA-Achr19 41173536 41174022 NUMBL chr19 40023093 40023309 EID2B chr1948305145 48306174 TPRX1 chr16 20359830 20360505 UMOD chr17 5643504656435862 RNF43 chr1 155178610 155179012 MTX1 chr10 46998897 47000240GPRIN2 chr19 1004686 1005532 GRIN3B chr10 71905568 71906151 TYSND1 chr1206680982 206681265 RASSF5 chr17 18918361 18918512 SLC5A10 chr7139167933 139168064 KLRG2 chr19 49850446 49850620 TEAD2 chr4 32575433257642 MSANTD1 chr10 135186743 135186842 ECHS1 chr7 5372281 5372407TNRC18 chr12 6777069 6777203 ZNF384 chr8 113240984 113241120 CSMD3 chr1910679188 10679329 CDKN2D chr19 984406 984555 WDR18 chr16 2059524 2059623ZNF598 chr16 2059622 2059736 ZNF598 chr19 1789555 1789722 ATP8B3 chr1175129889 175129988 KIAA0040 chr22 50920999 50921167 ADM2 chr7 10228471023021 CYP2W1 chr19 10431749 10431848 RAVER1 chr15 79092746 79092845ADAMTS7 chr1 248020555 248020715 TRIM58 chr17 48433882 48433981 XYLT2chr22 24121377 24121516 MMP11 chr12 25378547 25378707 KRAS chr1 2214980822149981 HSPG2 chr3 114057954 114058053 ZBTB20 chr15 102264303 102264477TARSL2 chr6 160769761 160769860 SLC22A3 chr6 137113136 137113249 MAP3K5chr16 88691009 88691153 ZC3H18 chr4 170678954 170679053 C4orf27 chr14105267578 105268105 ZBTB42 chr4 1388323 1389466 CRIPAK chr17 7011968270120347 SOX9 chr15 100252709 100252893 MEF2A chr11 44331308 44331531ALX4 chr17 7579311 7579537 TP53 chr3 150127941 150128485 TSC22D2 chr295537567 95537796 TEKT4 chrX 54209386 54209576 FAM120C chr19 5887917258880386 ZNF837 chr22 19968871 19969107 ARVCF chr20 48808010 48808450CEBPB chr12 7045137 7045925 ATN1 chr22 50615457 50616807 PANX2 chr5140248963 140250986 PCDHA11 chr11 65810208 65811054 GAL3ST3 chr1763533584 63533941 AXIN2 chr21 46929314 46929468 COL18A1 chr17 5644827156448394 RNF43 chr8 144874504 144874603 SCRIB chr8 145689544 145689660CYHR1 chr3 56591226 56591325 CCDC66 chr12 124886949 124887107 NCOR2 chr1204120808 204120953 ETNK2 chr9 138903634 138903747 NACC2 chr19 1762260117622700 PGLS chr18 34205515 34205642 FHOD3 chr19 50249868 50249967 TSKSchr22 50921108 50921207 ADM2 chr17 48619220 48619319 EPN3 chr11 7675151276751611 B3GNT6 chr16 84229435 84229581 ADAD2 chr19 49965140 49965293ALDH16A1 chr19 51015392 51015547 ASPDH chr2 241696750 241696849 KIF1AchrX 153657038 153657199 ATP6AP1 chr20 49411648 49411747 BCAS4 chr8145692341 145692493 KIFC2 chr7 150498638 150498812 TMEM176A chr5112164552 112164669 APC chr1 204228390 204228489 PLEKHA6 chr1 115258670115258781 NRAS chr4 113436024 113436123 NEUROG2 chr16 1820881 1820994NME3 chr6 82461335 82461758 FAM46A chr22 29837536 29837753 RFPL1 chr161270027 1270898 CACNA1H chr3 126260607 126261395 CHST13 chr2 239009072239009337 ESPNL chr4 4228254 4228473 OTOP1 chr15 90320120 90320492 MESP2chr2 56411816 56411994 CCDC85A chr6 102503254 102503433 GRIK2 chr742003929 42006215 GLI3 chr22 20130457 20131117 ZDHHC8 chr19 77472927747622 TRAPPC5 chr1 17266400 17266587 CROCC chr1 41976327 41976661HIVEP3 chr17 59489706 59489894 C17orf82 chr19 17836780 17838754 MAP1Schr14 77491801 77493810 IRF2BPL chr10 134999542 135000160 KNDC1 chr524487851 24488260 CDH10 chr15 93588263 93588738 RGMA chr3 122631701122631897 SEMA5B chr9 96051072 96051774 WNK2 chr2 171572939 171573733SP5 chr11 44286426 44286625 ALX4 chr14 24040237 24040437 JPH4 chr674161445 74161693 MB21D1 chr9 4117863 4118590 GLIS3 chr5 5381382953815535 SNX18 chr7 20824042 20824957 SP8 chrX 153688537 153688790PLXNA3 chr8 88885042 88886058 DCAF4L2 chr12 5153619 5154540 KCNA5 chr1931767495 31770449 TSHZ3 chr8 143694521 143695458 ARC chr16 8859961388601371 ZFPM1 chr8 144378009 144378869 ZNF696 chr15 65369394 65370354KBTBD13 chr11 76750643 76751605 B3GNT6 chr12 53045562 53045778 KRT2 chr5140228182 140230609 PCDHA9 chr16 87677885 87678577 JPH3 chr3 126733052126733175 PLXNA1 chr19 622286 622385 POLRMT chr22 38483130 38483271BAIAP2L2 chr9 136918393 136918563 BRD3 chr1 8421091 8421204 RERE chr16257711 6257816 RPL22 chr2 208633363 208633462 FZD5 chr7 7567746175677560 MDH2 chr11 379584 379683 B4GALNT4 chr13 39425847 39425976 FREM2chr19 44031239 44031338 ETHE1 chr2 202344754 202344898 STRADB chr538407050 38407204 EGFLAM chr2 211179634 211179766 MYL1 chr1 5230600352306102 NRD1 chr19 14083711 14083810 RFX1 chr18 48604661 48604790 SMAD4chr14 105070741 105070840 TMEM179 chr10 89692825 89692999 PTEN chr1089720678 89720824 PTEN chr6 166571879 166572046 T chr5 140174693140176839 PCDHA2 chr11 63767113 63767235 MACROD1 chr6 110746108110746285 SLC22A16 chr4 7043077 7044601 CCDC96 chr4 147560303 147560536POU4F2 chr17 70118880 70119113 SOX9 chr8 77616518 77618658 ZFHX4 chr1779898713 79899611 MYADML2 chrX 50350756 50350945 SHROOM4 chrX 8276344082764401 POU3F4 chr20 61443686 61444940 OGFR chr4 24801299 24801573 SOD3chr3 142840198 142841090 CHST2 chr12 53207441 53207638 KRT4 chr5140262267 140264211 PCDHA13 chr9 139943392 139943527 ENTPD2 chr3183951001 183951136 VWA5B2 chr2 46707801 46707900 TMEM247 chr1 152659327152659480 LCE2B chr2 87088917 87089016 CD8B chr22 38051312 38051481SH3BP1 chr11 6411896 6411995 SMPD1 chr17 260141 260300 C17orf97 chrX110987946 110988045 ALG13 chr16 58549882 58549981 SETD6 chr19 5184375851843857 VSIG10L chr2 176957772 176957871 HOXD13 chr18 3452173 3452272TGIF1 chrX 30326562 30327361 NR0B1 chr13 58298909 58299163 PCDH17 chr251254720 51255173 NRXN1 chr20 57766218 57769660 ZNF831 chr13 1975112419751658 TUBA3C chr19 48182629 48183772 GLTSCR1 chr1 237947095 237947554RYR2 chr8 142367086 142368005 GPR20 chr10 124895626 124895884 HMX3 chr1358206825 58209075 PCDH17 chr19 10224348 10224527 PPAN-P2RY11 chr5176025233 176026162 GPRIN1 chr5 140515026 140517383 PCDHB5 chr5140480344 140482621 PCDHB3 chr14 104641320 104644148 KIF26A chr296780973 96781614 ADRA2B chr2 226446656 226447604 NYAP2 chr20 4393295343933349 MATN4 chrX 120008789 120009265 CT47B1 chr5 140207725 140209879PCDHA6 chr8 77763206 77768391 ZFHX4 chr7 96653655 96653869 DLX5 chr12108985546 108986113 TMEM119 chr8 98289177 98289987 TSPYL5 chr13 4628737346288410 SPERT chr5 140255094 140257222 PCDHA12 chr18 76753062 76754866SALL3 chr2 1481012 1481232 TPO chr16 30666088 30666368 PRR14 chr48582726 8583313 GPR78 chr22 38476923 38477343 SLC16A8 chr4 134071393134073880 PCDH10 chr18 76753062 76755371 SALL3 chr7 53103553 53104199POM121L12 chr12 110019199 110019355 MVK chr1 117086970 117087119 CD58chr4 140811098 140811206 MAML3 chr8 120429023 120429177 NOV chr536035806 36035971 UGT3A2 chr2 74687408 74687551 WBP1 chr13 3832029138320455 TRPC4 chr16 12009241 12009340 GSPT1 chr16 77246457 77246556SYCE1L chr20 6032926 6033034 LRRN4 chr1 55081692 55081845 FAM151A chr12122685078 122685207 LRRC43 chr11 108117690 108117854 ATM chr17 50371815037291 USP6 chr7 102112900 102113056 LRWD1 chr3 139258468 139258567RBP1 chr12 95044117 95044216 TMCC3 chr5 5239832 5239994 ADAMTS16 chr633263902 33264001 RGL2 chr1 17265510 17265609 CROCC chr19 19129101913009 ADAT3 chr8 11831510 11831609 DEFB136 chr16 230483 230582 HBQ1chr6 166826249 166826375 RPS6KA2 chr10 126480292 126480402 METTL10 chr12121432052 121432151 HNF1A chr10 26446311 26446444 MYO3A chr1 4567191645672015 ZSWIM5 chr1 150530472 150530571 ADAMTSL4 chr4 8594554 8594653CPZ chr4 8603026 8603125 CPZ chr3 129293178 129293333 PLXND1 chr45862760 5862884 CRMP1 chr1 15850563 15850695 CASP9 chr12 2538021225380311 KRAS chr19 54754728 54754827 LILRB5 chr15 26026180 26026312ATP10A chr15 42371702 42371801 PLA2G4D chr14 29261265 29261364 C14orf23chr7 87564340 87564501 ADAM22 chr16 2070132 2070231 NPW chr9 135947042135947141 CEL chr9 133884777 133884876 LAMC3 chr19 41858871 41858970TGFB1 chr12 53183933 53184032 KRT3 chr4 126237800 126242717 FAT4 chr457843294 57843729 NOA1 chr19 47548478 47548679 NPAS1 chr1 160062149160062473 IGSF8 chr18 3456402 3456579 TGIF1 chr18 3456402 3456579 TGIF1chr17 1359313 1359412 CRK chr20 44642762 44642913 MMP9 chr19 4787884547878944 DHX34 chr17 41133021 41133120 RUNDC1 chr1 47685454 47685632TALI chr19 48197450 48197892 GLTSCR1 chr10 27702255 27703028 PTCHD3 chr3189526071 189526306 TP63 chr8 52320849 52322051 PXDNL chr1 9947003299470213 LPPR5 chr8 144997022 144999732 PLEC chr15 69325531 69325630NOX5 chr14 86087944 86089826 FLRT2 chr16 614762 615096 C16orf11 chr1735300116 35300417 LHX1 chr2 220283206 220283444 DES chr5 140572180140574513 PCDHB10 chr2 1651970 1653391 PXDN chr16 1840641 1842408 IGFALSchr12 54379054 54379706 HOXC10 chr7 154862696 154863298 HTR5A chr2177036378 177036844 HOXD3 chr10 135012167 135012731 KNDC1 chr7 8641567786416247 GRM3 chr7 43484121 43485149 HECW1 chr5 140557678 140559997PCDHB8 chr5 140220991 140223330 PCDHA8 chr5 140753704 140756051 PCDHGA6chr1 213031947 213032350 FLVCR1 chr8 10583340 10584034 SOX7 chr243451492 43452683 ZFP36L2 chr12 4479530 4479942 FGF23 chr17 36274723628884 GSG2 chr22 37964298 37964746 CDC42EP1 chr4 57180524 57182759KIAA1211 chr1 117078658 117078762 CD58 chr11 124750401 124750500 ROBO3chr11 64026609 64026708 PLCB3 chr16 88105674 88105818 BANP chr19 51106985110797 KDM4B chr11 76751543 76751642 B3GNT6 chr19 10407123 10407222ICAM5 chr1 27621004 27621120 WDTC1 chr5 158630536 158630642 RNF145 chr1955815034 55815194 BRSK1 chr5 112769461 112770529 TSSK1B chr22 1830093118301135 MICAL3 chr17 21318662 21319944 KCNJ12 chr1 117122056 117122290IGSF3 chr13 29598831 29600873 MTUS2 chr15 45007619 45007892 B2M chr187045630 87045903 CLCA4 chr16 10788328 10788537 TEKT5

TABLE 8 Chromosome Start (bp) End (bp) Gene chr7 148508714 148508813EZH2 chr6 134495648 134495770 SGK1 chr19 19260043 19260165 MEF2B chr637138900 37139211 PIM1 chr7 2985452 2985590 CARD11 chr6 2623472126234922 HIST1H1D chr3 38182243 38182342 MYD88 chr6 26031980 26032147HIST1H3B chr6 27834958 27835057 HIST1H1B chr19 10335365 10335542 S1PR2chr6 26056101 26056498 HIST1H1C chr18 60985340 60985897 BCL2 chr1763049621 63049729 GNA13 chr12 49426498 49426597 MLL2 chr6 3713873237138831 PIM1 chr6 37138342 37138441 PIM1 chr3 38182622 38182777 MYD88chr15 45003728 45003827 B2M chr6 26124500 26124827 HIST1H2AC chr626156732 26157169 HIST1H1E chr6 26250484 26250639 HIST1H3F chr19 65862196586366 CD70 chr15 45007783 45007882 B2M chr2 242066173 242066272 PASKchr2 96809958 96810091 DUSP2 chr17 63052507 63052611 GNA13 chr17 75770187577155 TP53 chrX 113965789 113965942 HTR2C chr1 120458108 120458207NOTCH2 chr3 176750758 176750924 TBL1XR1 chr17 62006764 62006863 CD79Bchr14 80328148 80328247 NRXN3 chr5 89923411 89923541 GPR98 chr1740951085 40951254 CNTD1 chr4 153249338 153249437 FBXW7 chr7 29638662963999 CARD11 chr12 92539163 92539311 BTG1 chr6 26158538 26158769HIST1H2BD chr6 27860546 27860875 HIST1H2AM chr1 2489781 2489907 TNFRSF14chr16 85936621 85936795 IRF8 chr6 26123760 26124023 HIST1H2BC chr627100943 27101241 HIST1H2AG chr6 27114217 27114519 HIST1H2BK chr626045793 26046018 HIST1H3C chr3 183273160 183273402 KLHL6 chr1 8573332685733577 BCL10 chr17 63010422 63010942 GNA13 chr6 27100151 27100263HIST1H2BJ chr7 5569165 5569288 ACTB chr3 187443286 187443417 BCL6 chr1942599939 42600081 POU2F2 chr1 2488088 2488187 TNFRSF14 chr17 75784017578530 TP53 chr12 113496043 113496165 DTX1 chr11 128391798 128391897ETS1 chr7 34724163 34724296 NPSR1 chr12 92537876 92538195 BTG1 chr8122626696 122627104 HAS2 chr16 11348700 11349138 SOCS1 chrX 15845841585235 P2RY8 chr15 39544367 39544819 C15orf54 chr6 27861294 27861585HIST1H2BO chr8 114185958 114186078 CSMD3 chr8 57228764 57228900 SDR16C5chr6 14118180 14118296 CD83 chr19 19261467 19261566 MEF2B chr10 9878100698781170 SLIT1 chr5 32090982 32091118 PDZD2 chr2 125555706 125555805CNTNAP5 chr5 7414684 7414783 ADCY2 chr11 17482173 17482272 ABCC8 chr588119528 88119627 MEF2C chr1 173819464 173819617 DARS2 chr1 181727082181727247 CACNA1E chr7 148506392 148506491 EZH2 chr1 117078701 117078800CD58 chr1 117086988 117087131 CD58 chr7 82763869 82763975 PCLO chr1213769407 13769569 GRIN2B chr5 145393394 145393518 SH3RF2 chr19 4376601843766117 PSG9 chr20 25003575 25003728 ACSS1 chr11 60229847 60230006MS4A1 chr11 89531416 89531515 TRIM49 chr8 101730371 101730470 PABPC1chr15 66729083 66729230 MAP2K1 chr4 24544556 24544655 DHX15 chr163786650 3786816 CREBBP chr6 134493799 134493912 SGK1 chr3 6052259260522695 FHIT chr1 9784333 9784479 PIK3CD chr19 10934463 10934575 DNM2chr15 26806082 26806181 GABRB3 chr17 7577498 7577608 TP53 chr5 112176808112176907 APC chr1 82408728 82408842 LPHN2 chr1 190195307 190195406FAM5C chr7 2977540 2977666 CARD11 chr11 118343087 118343186 MLL chr316419284 16419420 RFTN1 chr6 27839714 27839833 HIST1H3I chr11 4920819549208321 FOLH1 chr11 18194889 18195049 MRGPRX4 chrX 102931279 102931380MORF4L2 chr8 3141777 3141876 CSMD1 chr5 149677048 149677147 ARSI chrX70784450 70784603 OGT chr3 38181907 38182033 MYD88 chr9 3580070535800838 NPR2 chr19 21476425 21476524 ZNF708 chr16 85954792 85954891IRF8 chr4 158257566 158257665 GRIA2 chr11 14899653 14899752 CYP2R1 chr1830349821 30350141 KLHL14 chr22 23523625 23524360 BCR chr9 41184654118649 GLIS3 chr5 124079896 124080638 ZNF608 chrX 92927664 92928269NAP1L3 chr1 167096068 167096479 DUSP27 chr4 115997524 115997764 NDST4chr6 27777852 27778102 HIST1H3H chrX 86773014 86773267 KLHL4 chr7138601542 138601795 KIAA1549 chr1 179562711 179562985 TDRD5 chr8128750609 128751108 MYC chr4 154624731 154625043 TLR2 chr1 149857823149858147 HIST2H2BE chr17 51900491 51900825 KIF2B chr8 116616196116616816 TRPS1 chr4 88583982 88584348 DMP1 chrX 41586526 41586894 GPR82chr14 55241653 55241762 SAMD4A chr8 85774530 85774688 RALYL chr589949226 89949325 GPR98 chr7 91503615 91503714 MTERF chr2 136872579136872678 CXCR4 chr5 80643592 80643749 ACOT12 chr14 21897075 21897174CHD8 chr22 41525893 41526007 EP300 chr4 126319938 126320070 FAT4 chr176012926 6013086 WSCD1 chr9 95085704 95085803 NOL8 chr2 11354943 11355042ROCK2 chr1 59844415 59844514 FGGY chr13 37401779 37401890 RFXAP chr1248190799 48190925 HDAC7 chr2 198353036 198353135 HSPD1 chr10 4842881848428917 GDF10 chr17 26961625 26961724 KIAA0100 chr1 150915478 150915577SETDB1 chr7 1527451 1527550 INTS1 chr3 93755496 93755595 ARL13B chr17700459 7700613 CAMTA1 chr11 130784481 130784580 SNX19 chr2 16878371687936 PXDN chrX 138886629 138886758 ATP11C chr10 121677458 121677557SEC23IP chr16 58562378 58562552 CNOT1 chr2 75425942 75426041 TACR1 chr6102337597 102337696 GRIK2 chr9 35376114 35376213 UNC13B chr15 5252967852529843 MYO5C chr4 100784919 100785018 DAPP1 chrX 135288683 135288782FHL1 chr3 50005082 50005181 RBM6 chr19 15366097 15366196 BRD4 chr3183209816 183209915 KLHL6 chr3 183210322 183210468 KLHL6 chr21 3516976435169863 ITSN1 chr12 66923602 66923701 GRIP1 chr8 68931783 68931906PREX2 chr9 119202908 119203007 ASTN2 chr9 23701450 23701549 ELAVL2 chr5121758997 121759096 SNCAIP chr8 113303749 113303869 CSMD3 chr12 64397636439877 TNFRSF1A chr2 141245185 141245308 LRP1B chr2 141291589 141291709LRP1B chr2 142004794 142004923 LRP1B chr10 22653781 22653948 SPAG6 chr12119942896 119942995 CCDC60 chr10 115365936 115366041 NRAP chr4 159634270159634412 PPID chr1 160319338 160319460 NCSTN chr12 132683716 132683815GALNT9 chr11 111715323 111715446 ALG9 chr18 28714581 28714715 DSC1 chr2236661645 36661744 APOL1 chrX 125955246 125955345 CXorf64 chr18 2152610721526248 LAMA3 chr7 21550781 21550880 SP4 chr8 124975517 124975638FER1L6 chr8 124195469 124195568 FAM83A chr1 91740276 91740375 HFM1 chr1229772414 229772513 URB2 chr12 49943314 49943413 KCNH3 chr6 7200618172006280 OGFRL1 chr13 32907254 32907353 BRCA2 chr17 41847111 41847210DUSP3 chr8 99441265 99441364 KCNS2 chr4 85626546 85626664 WDFY3 chr485687017 85687116 WDFY3 chr4 85717707 85717806 WDFY3 chr12 5759840957598532 LRP1 chr2 149528565 149528664 EPC2 chr2 122204912 122205083CLASP1 chr11 66008985 66009084 PACS1 chr6 155458538 155458637 TIAM2 chr8124664138 124664237 KLHL38 chr2 202264099 202264216 TRAK2 chr21 3783335137833450 CLDN14 chr17 74276367 74276532 QRICH2 chr17 1563134 1563295PRPF8 chr1 92470012 92470111 BRDT chr16 14334156 14334255 MKL2 chr12115120815 115120932 TBX3 chr12 108013890 108013989 BTBD11 chr6 152697629152697728 SYNE1 chr8 110463284 110463383 PKHD1L1 chr5 32074455 32074554PDZD2 chr15 65917821 65917920 SLC24A1 chr14 32615457 32615556 ARHGAP5chr2 103148789 103148888 SLC9A4 chr5 79733658 79733757 ZFYVE16 chr1492088143 92088242 CATSPERB chr15 89056238 89056337 DET1 chr1 3585781235857953 ZMYM4 chr6 38743648 38743747 DNAH8 chr2 125204372 125204471CNTNAP5 chr2 125669029 125669128 CNTNAP5 chr5 36671219 36671318 SLC1A3chr4 3419114 3419268 RGS12 chr8 110984837 110984940 KCNV1 chr11 6464560264645701 EHD1 chr7 31378618 31378717 NEUROD6 chr8 35544062 35544227UNC5D chr17 33288569 33288668 ZNF830 chr19 37210027 37210126 ZNF567 chr4187524795 187524894 FAT1 chr20 3321138 3321237 C20orf194 chr1 109795535109795634 CELSR2 chr11 100863129 100863281 TMEM133 chr5 6759103667591135 PIK3R1 chr9 37740424 37740523 FRMPD1 chrX 32663134 32663233 DMDchr2 169781166 169781313 ABCB11 chr18 64239223 64239322 CDH19 chr8623942 624041 ERICH1 chr9 82319697 82319817 TLE4 chr20 35812674 35812773RPN2 chr14 35873721 35873820 NFKBIA chr6 83838787 83838886 DOPEY1 chr273675936 73676035 ALMS1 chr11 73715528 73715630 UCP3 chr6 126210203126210302 NCOA7 chr20 36963988 36964087 BPI chr6 26252135 26252245HIST1H2BH chr2 69627576 69627675 NFU1 chr20 480476 480578 CSNK2A1 chr7140453074 140453193 BRAF chr11 7021848 7021947 ZNF214 chr18 3242825332428352 DTNA chr11 70271422 70271521 CTTN chr15 50784917 50785016 USP8chr3 164730749 164730848 SI chr1 27105515 27105614 ARID1A chr17 1800157818001677 DRG2 chr11 125472697 125472843 STT3A chr18 56390352 56390451MALT1 chr4 186380380 186380479 CCDC110 chr1 160850943 160851102 ITLN1chr5 131825077 131825176 IRF1 chr10 129868564 129868714 PTPRE chr1054527901 54528035 MBL2 chr2 171071238 171071338 MYO3B chr18 31938153193956 MYOM1 chr1 1290159 1290330 MXRA8 chr3 2924828 2924931 CNTN4 chr552096606 52096705 PELO chr10 90773913 90774012 FAS chr13 2535243225352544 RNF17 chr7 80285855 80286016 CD36 chr5 132084038 132084167CCNI2 chr7 64439781 64439907 ZNF117 chr16 84089609 84089740 MBTPS1 chr1939959395 39959501 SUPT5H chr19 19576162 19576261 GATAD2A chr4 155490817155490916 FGB chr4 66231649 66231775 EPHA5 chr1 111957552 111957666OVGP1 chr6 105243453 105243560 HACE1 chr11 118770652 118770757 BCL9Lchr2 55756013 55756128 CCDC104 chr2 27319583 27319682 KHK chr14 8174337781743476 STON2 chr7 82784463 82784562 PCLO chrX 53573396 53573553 HUWE1chr2 200233327 200233430 SATB2 chr8 77762480 77762598 ZFHX4 chr137945890 37946030 ZC3H12A chr1 37948734 37948833 ZC3H12A chr5 1957172319571822 CDH18 chr9 134497232 134497374 RAPGEF1 chr10 30747012 30747165MAP3K8 chr2 27247017 27247116 MAPRE3 chr6 76385659 76385758 SENP6 chr279313492 79313630 REG1B chr7 129806268 129806367 TMEM209 chr12 3968822239688321 KIF21A chr10 101841194 101841293 CPN1 chr17 40475021 40475161STAT3 chr8 75898251 75898352 CRISPLD1 chr10 131640350 131640449 EBF3chr7 14758166 14758310 DGKB chr9 101904817 101904985 TGFBR1 chr3100557041 100557140 ABI3BP chr3 100604998 100605097 ABI3BP chr1958131754 58131853 ZNF134 chr3 146311834 146311933 PLSCR5 chr16 5350385553503954 RBL2 chr1 154931304 154931403 PYGO2 chr6 80223202 80223301 LCA5chr1 24840825 24840924 RCAN3 chr6 27277340 27277439 POM121L2 chr14102822104 102822234 CINP chr12 57496608 57496707 STAT6 chrX 153997441153997585 DKC1 chr12 26553060 26553191 ITPR2 chr12 26755302 26755428ITPR2 chr5 35047903 35048002 AGXT2 chr14 50889816 50889915 MAP4K5 chrX154159899 154159998 F8 chr9 34635668 34635767 SIGMAR1 chr7 113558284113558383 PPP1R3A chr6 27799221 27799320 HIST1H4K chr2 152518644152518743 NEB chr1 236718595 236718764 HEATR1 chr17 78343567 78343667RNF213 chr7 122634962 122635061 TAS2R16 chr6 394881 394980 IRF4 chr5137599979 137600078 GFRA3 chr2 189849534 189849633 COL3A1 chr1 185269130185269262 IVNS1ABP chr5 83259023 83259179 EDIL3 chr12 53900804 53900926NPFF chr1 231334792 231334915 TRIM67 chr17 5037181 5037291 USP6 chr3151165871 151165970 IGSF10 chr19 55143390 55143489 LILRB1 chr6 2621676726216866 HIST1H2BG chr1 12785590 12785705 AADACL3 chrX 70612724 70612844TAF1 chr15 91019894 91020050 IQGAP1 chr3 112324459 112324558 CCDC80 chr5149631536 149631635 CAMK2A chr17 50235066 50235165 CA10 chr4 3607531036075445 ARAP2 chr15 99250974 99251073 IGF1R chr14 65259812 65259911SPTB chr7 47944073 47944172 PKD1L1 chr21 34166539 34166638 C21orf62 chr3173322726 173322825 NLGN1 chr10 25313261 25313360 THNSL1 chr1 201038599201038729 CACNA1S chr8 144990426 144990525 PLEC chr13 28197172 28197271POLR1D chr12 41900352 41900451 PDZRN4 chr20 139395 139494 DEFB127 chr7146997232 146997382 CNTNAP2 chr6 26443795 26443894 BTN3A3 chr16 3009378030093879 PPP4C chr10 22030840 22030939 MLLT10 chr15 44120405 44120504WDR76 chr16 11076734 11076848 CLEC16A chr6 49937259 49937358 DEFB113chr7 127014541 127014640 ZNF800 chr3 37514844 37514951 ITGA9 chr5140221244 140221343 PCDHA8 chr19 1055059 1055158 ABCA7 chr2 238275682238275781 COL6A3 chr2 238280539 238280638 COL6A3 chr6 27782778 27782877HIST1H2BM chr16 72833925 72834028 ZFHX3 chr9 78686641 78686814 PCSK5chr13 26620899 26620998 SHISA2 chr15 66727404 66727503 MAP2K1 chr521783466 21783603 CDH12 chr7 73950496 73950605 GTF2IRD1 chr7 9273351892733617 SAMD9 chr20 57581376 57581540 CTSZ chr1 116283348 116283449CASQ2 chr22 50471719 50471818 TTLL8 chr7 75192479 75192578 HIP1 chr1958965614 58965713 ZNF324B chr11 31392295 31392406 DNAJC24 chr5 8036918180369280 RASGRF2 chr8 116426513 116426636 TRPS1 chr8 116599420 116599519TRPS1 chr20 32341030 32341129 ZNF341 chr21 28338441 28338573 ADAMTS5chr10 105209455 105209554 CALHM2 chr16 29824386 29824485 PRRT2 chr1454886703 54886802 CDKN3 chr2 116534779 116534878 DPP10 chr12 5639754156397640 SUOX chr1 151339198 151339297 SELENBP1 chr21 18981289 18981462BTG3 chr3 196529887 196530035 PAK2 chrX 118540596 118540695 SLC25A43chr20 48127564 48127716 PTGIS chr20 3543855 3544010 ATRN chr5 3570912535709224 SPEF2 chr5 35807232 35807355 SPEF2 chr6 26199865 26199964HIST1H2BF chr2 160136377 160136476 WDSUB1 chr10 96014649 96014806 PLCE1chr10 123987351 123987523 TACC2 chr6 41899465 41899568 BYSL chr1016996387 16996547 CUBN chr7 122809280 122809379 SLC13A1 chr6 8492503484925133 KIAA1009 chr12 15813547 15813674 EPS8 chr16 5041881 5041980SEC14L5 chr2 48028009 48028108 MSH6 chr2 170735009 170735108 UBR3 chr2234545387 234545561 UGT1A10 chr2 9770341 9770440 YWHAQ chr1 1272664412726743 AADACL4 chrX 119509339 119509438 ATP1B4 chr7 94740570 94740703PPP1R9A chr5 39138726 39138825 FYB chr17 4007975 4008074 ZZEF1 chr12111089106 111089205 HVCN1 chr22 32193585 32193689 DEPDC5 chr19 3899693038997029 RYR1 chr1 1421489 1421615 ATAD3B chr14 37154076 37154175SLC25A21 chr3 140281652 140281798 CLSTN2 chr17 38447286 38447385 CDC6chr6 51617998 51618151 PKHD1 chr10 21076130 21076237 NEBL chr11 6510886965109033 DPF2 chr18 52899739 52899902 TCF4 chrX 151819978 151820077GABRQ chrX 70347869 70347968 MED12 chr19 52537324 52537423 ZNF432 chr2132638490 32638633 TIAM1 chr2 230861466 230861639 FBXO36 chr1 236966822236966921 MTR chrX 84526133 84526234 ZNF711 chr20 55966758 55966857RBM38 chr4 7728506 7728630 SORCS2 chrX 153628143 153628282 RPL10 chr2030681665 30681819 HCK chr2 9514894 9514993 ASAP2 chr15 50223389 50223488ATP8B4 chrX 140996390 140996491 MAGEC1 chr16 3788559 3788673 CREBBPchr16 3808854 3808973 CREBBP chr6 134491958 134492057 SGK1 chr6134494403 134494502 SGK1 chr6 134494599 134494704 SGK1 chr6 134495130134495229 SGK1 chr4 151817527 151817626 LRBA chr3 23934688 23934787NKIRAS1 chrX 13680790 13680889 TCEANC chr19 15164540 15164639 CASP14chr8 24813192 24813291 NEFL chr12 122658390 122658539 IL31 chr6 7085971970859818 COL19A1 chrX 119059299 119059398 NKAP chr12 18800809 18800962PIK3C2G chr8 48777075 48777174 PRKDC chr7 100172827 100172926 LRCH4 chr9133948158 133948257 LAMC3 chr17 62006585 62006684 CD79B chr13 114009637114009796 GRTP1 chr6 73043453 73043552 RIMS1 chr3 187447106 187447205BCL6 chr5 176522495 176522594 FGFR4 chr18 6311538 6311637 L3MBTL4 chr1595001365 95001475 MCTP2 chr15 75798216 75798316 PTPN9 chr2 215843515215843614 ABCA12 chr2 32865336 32865477 TTC27 chr3 27216096 27216195NEK10 chr4 62813853 62813952 LPHN3 chr11 9597421 9597520 WEE1 chr6106552825 106552924 PRDM1 chr3 107517429 107517528 BBX chr10 128923737128923865 DOCK1 chr13 111109686 111109785 COL4A2 chr3 122338609122338708 PARP15 chr22 17690369 17690468 CECR1 chr4 83279811 83279973HNRNPD chr4 76572212 76572341 G3BP2 chr5 179201689 179201788 MAML1 chr3123385065 123385193 MYLK chr11 5529961 5530060 UBQLN3 chr11 5715604957156181 PRG2 chr6 151673552 151673651 AKAP12 chr18 54547185 54547284WDR7 chr8 15519664 15519805 TUSC3 chr3 196288280 196288379 WDR53 chr1847101791 47101899 LIPG chr19 56300172 56300343 NLRP11 chr9 8653043486530533 KIF27 chr8 25715787 25715886 EBF2 chr22 41320365 41320486XPNPEP3 chr2 170042198 170042297 LRP2 chr12 18891329 18891491 CAPZA3chr1 223465866 223465965 SUSD4 chr1 2491261 2491417 TNFRSF14 chr617856257 17856356 KIF13A chr8 86354301 86354420 CA3 chr1 9434185994341958 DNTTIP2 chr2 177033872 177033971 HOXD3 chr2 128409047 128409146GPR17 chr14 21269809 21269908 RNASE1 chr17 7579314 7579413 TP53 chr4160274689 160274788 RAPGEF2 chr1 183498026 183498177 SMG7 chr7 105738160105738259 SYPL1 chr10 118220477 118220597 PNLIPRP3 chr6 3294316032943298 BRD2 chr19 8028461 8028560 ELAVL1 chr2 211542610 211542709 CPS1chr10 103870285 103870458 LDB1 chrX 18528907 18529006 CDKL5 chr1573067306 73067405 ADPGK chr11 124524550 124524689 SIAE chr14 4712070647120805 RPL10L chr12 32875343 32875442 DNM1L chr15 41797166 41797265LTK chr18 44139410 44139565 LOXHD1 chr11 68480737 68480875 MTL5 chr162327222 62327339 INADL chr14 73576049 73576200 RBM25 chr15 4138422441384380 INO80 chrX 105152975 105153074 NRK chr17 79478986 79479112ACTG1 chr6 55659076 55659225 BMP5 chr19 1376496 1376595 MUM1 chr1954377264 54377408 MYADM chr12 83289884 83289983 TMTC2 chr2 165557109165557208 COBLL1 chr17 29314961 29315124 RNF135 chr16 77326994 77327093ADAMTS18 chr6 41877064 41877163 MED20 chr5 11236802 11236935 CTNND2 chr511364764 11364863 CTNND2 chr4 88011129 88011228 AFF1 chr8 139601454139601553 COL22A1 chr17 28530189 28530357 SLC6A4 chr19 16594755 16594854CALR3 chr9 74597635 74597734 C9orf85 chr3 49060488 49060605 NDUFAF3chr14 64628861 64628990 SYNE2 chr1 154076518 154076617 NUP210L chr1115829207 115829306 NGF chr12 21032377 21032476 SLCO1B3 chr3 5028982850289970 GNAI2 chr6 101100600 101100765 ASCC3 chrX 82763773 82763872POU3F4 chr14 21792809 21792927 RPGRIP1 chr15 91454076 91454191 MAN2A2chr1 212792672 212792771 ATF3 chr7 2976714 2976813 CARD11 chr7 29839822984143 CARD11 chr9 101797295 101797436 COL15A1 chr6 26217266 26217365HIST1H2AE chr1 180257497 180257652 ACBD6 chr3 183474315 183474477 YEATS2chr7 82997199 82997298 SEMA3E chr19 964872 964971 ARID3A chr18 4737985847379957 MYO5B chr2 190561034 190561133 ANKAR chr4 38830591 38830690TLR6 chr17 5366848 5367009 DHX33 chr4 52894133 52894265 SGCB chr757529173 57529272 ZNF716 chr1 196715017 196715116 CFH chr12 2539820725398318 KRAS chrX 77245260 77245359 ATP7A chr4 144797907 144798008 GYPEchr11 111613246 111613389 PPP2R1B chr20 10622189 10622288 JAG1 chr627833334 27833433 HIST1H2AL chr10 75037936 75038095 TTC18 chr4 4174817741748324 PHOX2B chr7 154790359 154790494 PAXIP1 chr12 59276650 59276814LRIG3 chr10 91514274 91514430 KIF20B chrX 19702095 19702194 SH3KBP1 chr133134339 33134455 RBBP4 chr16 84050214 84050313 SLC38A8 chr13 3332997933330094 PDS5B chr6 40360213 40360338 LRFN2 chr15 42178024 42178123SPTBN5 chr15 42182286 42182403 SPTBN5 chr15 75705265 75705364 SIN3A chr843211901 43212038 POTEA chr15 45059892 45059991 TRIM69 chr1 145663185145663284 RNF115 chr13 107822916 107823015 FAM155A chr12 6406206264062165 DPY19L2 chr1 207133970 207134069 FCAMR chr18 28934566 28934665DSG1 chr16 89986545 89986644 TUBB3 chr19 4219587 4219755 ANKRD24 chr4110221723 110221822 COL25A1 chr9 79829223 79829322 VPS13A chr14 6047033560470434 LRRC9 chr5 141059826 141059925 ARAP3 chr7 34097670 34097775BMPER chr7 34118612 34118757 BMPER chr16 67645458 67645557 CTCF chr471024052 71024151 C4orf40 chr1 183085901 183086038 LAMC1 chr6 4190366941903768 CCND3 chr5 137733914 137734032 KDM3B chr19 12976129 12976295MAST1 chr19 18547782 18547915 ISYNA1 chr18 28980843 28980983 DSG4 chr1828989414 28989554 DSG4 chr1 215408276 215408375 KCNK2 chr8 1744720517447304 PDGFRL chr15 76726408 76726507 SCAPER chr17 38935753 38935879KRT27 chr4 53773623 53773758 SCFD2 chr9 8517993 8518092 PTPRD chr1844470498 44470597 PIAS2 chr1 115142824 115142973 DENND2C chr1 204956545204956668 NFASC chr12 112321438 112321537 MAPKAPK5 chr4 3950549439505605 UGDH chr20 8637830 8637931 PLCB1 chr8 56986618 56986718 RPS20chr15 101586185 101586357 LRRK1 chr21 28213316 28213484 ADAMTS1 chr2128216821 28216939 ADAMTS1 chr13 99361820 99361919 SLC15A1 chr11 4773896947739068 FNBP4 chr3 51929063 51929162 IQCF1 chr11 108385066 108385165EXPH5 chrX 83129052 83129151 CYLC1 chr19 12902639 12902790 JUNB chr1531324879 31324978 TRPM1 chr4 106157669 106157768 TET2 chr4 106157669106157768 TET2 chr14 30093357 30093464 PRKD1 chr10 29162152 29162251C10orf126 chr14 23887408 23887507 MYH7 chr1 237777351 237777450 RYR2chr1 237872333 237872432 RYR2 chr1 237955374 237955473 RYR2 chr1490650530 90650629 KCNK13 chr6 56401576 56401738 DST chr6 5650674456506899 DST chr5 86703814 86703913 CCNH chr20 50408497 50408596 SALL4chr2 62729571 62729685 TMEM17 chr1 94485168 94485267 ABCA4 chr9 1312207713122176 MPDZ chr9 13125254 13125353 MPDZ chr9 13222236 13222335 MPDZchr6 66205085 66205184 EYS chrX 79947321 79947477 BRWD3 chr6 4315319343153348 CUL9 chr22 16287258 16287357 POTEH chr16 30777747 30777859RNF40 chr6 56880036 56880135 BEND6 chr10 73337660 73337759 CDH23 chr675965903 75966002 TMEM30A chr6 75969062 75969206 TMEM30A chr3 3994230739942417 MYRIP chr10 103920213 103920312 NOLC1 chr14 103438375 103438474CDC42BPB chr19 40884019 40884118 PLD3 chr5 137520200 137520365 KIF20Achr12 34179714 34179813 ALG10 chr8 1513979 1514078 DLGAP2 chr1 151508712151508821 CGN chr12 7087502 7087669 LPCAT3 chr12 107144432 107144571RFX4 chr2 237032525 237032624 AGAP1 chr7 33035844 33035943 FKBP9 chr1850936909 50937008 DCC chr1 206239399 206239498 C1orf186 chr6 107780193107780292 PDSS2 chr2 80801287 80801439 CTNNA2 chr6 26020776 26020886HIST1H3A chr3 160960295 160960441 NMD3 chr13 111372024 111372140 ING1chr12 12037378 12037521 ETV6 chr2 168074675 168074810 XIRP2 chr1034985245 34985347 PARD3 chr5 135382023 135382184 TGFBI chr1 3547255135472699 ZMYM6 chr5 101627159 101627258 SLCO4C1 chr5 13777310 13777464DNAH5 chr3 38592168 38592289 SCN5A chr4 157688996 157689095 PDGFC chr2178481432 178481531 TTC30A chr5 16453121 16453265 ZNF622 chr9 3338576833385867 AQP7 chrX 26157157 26157552 MAGEB18 chr13 51915293 51915474SERPINE3 chr18 13825985 13826401 MC5R chr10 15138569 15138755 C10orf111chr1 215848722 215848909 USH2A chr18 64176264 64176451 CDH19 chr11118764907 118765342 CXCR5 chr19 13264454 13264647 IER2 chr6 167753817167754016 TTLL2 chr8 105509842 105510291 LRP12 chr14 44974728 44975179FSCB chr5 137801551 137801752 EGR1 chr14 26917682 26917884 NOVA1 chrX91133711 91133913 PCDH11X chr2 129025756 129025960 HS6ST1 chr11 6562347565623681 CFL1 chr4 126411276 126411748 FAT4 chrX 102529117 102529327TCEAL5 chr15 56390324 56390539 RFX7 chr2 155711425 155711641 KCNJ3 chr11110451414 110451631 ARHGAP20 chr18 74728958 74729176 MBP chr3 168834000168834219 MECOM chr12 49723932 49724157 TROAP chrX 125686292 125686517DCAF12L1 chr16 2165393 2165622 PKD1 chr16 2049881 2050111 ZNF598 chr1824496280 24496517 CHST9 chr4 52861378 52861618 LRRC66 chr5 140346836140347078 PCDHAC2 chr4 156135335 156135577 NPY2R chr20 49626535 49626782KCNG1 chr5 5182162 5182410 ADAMTS16 chr8 13357238 13357493 DLC1 chr277746652 77746909 LRRTM4 chr1 114680207 114680472 SYT6 chr3 5252156652521836 NISCH chrX 72667220 72667491 CDX4 chr7 89856395 89856678 STEAP2chr6 139694759 139695043 CITED2 chr5 139908231 139908521 ANKHD1-EIF4EBP3 chr7 119915452 119915743 KCND2 chr19 53013964 53014256 ZNF578chr1 28800091 28800385 PHACTR4 chr19 53384711 53385007 ZNF320 chr10123970892 123971189 TACC2 chr5 140482099 140482396 PCDHB3 chr11100998623 100998923 PGR chr8 107719046 107719353 OXR1 chr9 2795020027950510 LINGO2 chrX 151935296 151935608 MAGEA3 chr3 156763153 156763466LEKR1 chr18 65179922 65181766 DSEL chr7 110762993 110764937 LRRN3 chr430725160 30725981 PCDH7 chr1 226923743 226925140 ITPKB chr4 188924172188924867 ZFP42 chr9 16435555 16436253 BNC2 chr13 84453589 84455218SLITRK1 chr5 140207820 140209113 PCDHA6 chr13 58207180 58209076 PCDH17chrX 73962177 73963052 KIAA2022 chrX 27998915 27999442 DCAF8L1 chr1346357646 46358180 SIAH3 chrX 109694664 109695215 RGAG1 chrX 3582063435821206 MAGEB16 chr3 7620283 7620915 GRM7 chr19 22362809 22363934ZNF676 chr5 75913775 75914411 F2RL2 chr4 80327835 80328489 GK2 chr1227842666 227843353 ZNF678 chr2 1652069 1652771 PXDN chr4 3877546338775787 TLR10 chr6 26197079 26197411 HIST1H3D chr8 98289660 98289998TSPYL5 chr8 104897619 104898393 RIMS2 chr18 64172177 64172523 CDH19chr12 86198768 86199549 RASSF9 chr19 44610962 44611310 ZNF224 chr1523931598 23931947 NDN chr17 61432393 61432746 TANC2 chr3 165548257165548615 BCHE chr10 55581999 55582810 PCDH15 chr1 86591496 86591856COL24A1 chr19 56423331 56423694 NLRP13 chr17 2202994 2203359 SMG6 chrX91090526 91090897 PCDH11X chr14 23344753 23345125 LRP10 chr6 107390142107390514 BEND3 chr20 23028428 23028801 THBD chr19 21366346 21366722ZNF431 chr15 86312120 86312500 KLHL25 chr15 70961395 70961785 UACA chr339227655 39228049 XIRP1 chr2 108626767 108627163 SLC5A7 chrX 141291116141291519 MAGEC2 chr6 94120276 94120685 EPHA7 chr4 187509930 187510340FAT1 chr6 28213078 28213491 ZKSCAN4 chr8 18729496 18729912 PSD3 chr1190067191 190068138 FAM5C chr2 198950504 198950925 PLCL1 chr3 150127293150127719 TSC22D2 chr1 61553848 61554286 NFIA chr19 58639971 58640431ZNF329 chr5 140181824 140182291 PCDHA3 chr16 30456075 30456549 SEPHS2chr22 20819371 20819850 KLHL22 chr13 32912282 32912764 BRCA2 chr1721318946 21319434 KCNJ12 chr5 138208750 138209240 LRRTM2 chr5 129520740129521232 CHSY3 chr8 8748736 8749231 MFHAS1 chr2 186653719 186654217FSIP2 chr19 42752828 42753332 ERF chr5 140553945 140554450 PCDHB7 chr8103663550 103664076 KLF10 chr5 140516575 140517107 PCDHB5 chr15 2381106323811606 MKRN3 chr19 35232198 35232754 ZNF181 chr1 29069584 29070145YTHDF2 chr7 106508558 106509120 PIK3CG chr17 18022175 18022740 MYO15Achr16 2812143 2812722 SRRM2 chr11 129739436 129740022 NFRKB chr1151377896 151378497 POGZ chr1 14108395 14109018 PRDM2 chr1 7503848475039109 C1orf173 chrX 26212155 26212785 MAGEB6 chr7 82387890 82388031PCLO chr7 82453578 82453677 PCLO chr6 37138548 37138655 PIM1 chr637140805 37140904 PIM1 chr4 126328000 126328099 FAT4 chr4 126336747126336846 FAT4 chr4 126337678 126337777 FAT4 chr4 126389661 126389760FAT4 chr8 113266468 113266567 CSMD3 chr8 113308061 113308235 CSMD3 chr8113314021 113314195 CSMD3 chr8 113332120 113332219 CSMD3 chr8 113347557113347703 CSMD3 chr8 113348910 113349009 CSMD3 chr8 113353773 113353872CSMD3 chr8 113364644 113364763 CSMD3 chr8 113569046 113569145 CSMD3 chr8113585729 113585886 CSMD3 chr8 113599294 113599464 CSMD3 chr8 113668445113668544 CSMD3 chr8 113702216 113702315 CSMD3 chr8 113812390 113812503CSMD3 chr8 113871373 113871495 CSMD3 chr8 114448912 114449011 CSMD3chr12 49416049 49416148 MLL2 chr12 49418360 49418491 MLL2 chr12 4942059349420692 MLL2 chr12 49427948 49428047 MLL2 chr12 49433338 49433437 MLL2chr12 49437982 49438087 MLL2 chr12 49438185 49438305 MLL2 chr12 4944445049444549 MLL2 chr12 49447258 49447424 MLL2 chr7 2978312 2978465 CARD11chr7 2979449 2979548 CARD11 chr7 2987232 2987331 CARD11 chr7 148523590148523689 EZH2 chr1 2489164 2489273 TNFRSF14 chr1 2493111 2493254TNFRSF14 chr17 7578176 7578289 TP53 chr6 56327843 56327954 DST chr656330875 56330993 DST chr6 56368794 56368896 DST chr6 56458548 56458647DST chr6 56466227 56466326 DST chr6 56499259 56499414 DST chr6 5649959856499751 DST chr6 56501352 56501451 DST chr6 56515723 56515830 DST chr2141072503 141072668 LRP1B chr2 141259305 141259404 LRP1B chr2 141299447141299546 LRP1B chr2 141356244 141356343 LRP1B chr2 141459289 141459414LRP1B chr2 141680580 141680679 LRP1B chr2 141819709 141819808 LRP1B chr1215799117 215799216 USH2A chr1 215813913 215814012 USH2A chr1 215844296215844395 USH2A chr1 215901422 215901521 USH2A chr1 215953269 215953368USH2A chr1 215955383 215955538 USH2A chr1 215960043 215960142 USH2A chr1216052082 216052181 USH2A chr1 216108069 216108168 USH2A chr1 216262354216262481 USH2A chr1 216270424 216270555 USH2A chr1 216462621 216462752USH2A chr1 216497541 216497640 USH2A chr19 19257550 19257684 MEF2B chr4187530336 187530474 FAT1 chr4 187534231 187534330 FAT1 chr4 187549398187549497 FAT1 chr4 187557842 187557941 FAT1 chr6 134492772 134492871SGK1 chr1 185833601 185833760 HMCN1 chr1 185969270 185969369 HMCN1 chr1185972849 185972976 HMCN1 chr1 186039743 186039889 HMCN1 chr1 186062637186062736 HMCN1 chr1 186083110 186083255 HMCN1 chr1 186135939 186136074HMCN1 chr1 186143645 186143774 HMCN1 chr1 186158943 186159042 HMCN1 chr8116631744 116631843 TRPS1 chr16 3789578 3789725 CREBBP chr16 38237723823871 CREBBP chr16 3900300 3900399 CREBBP chr16 85945170 85945269 IRF8chr5 89943517 89943616 GPR98 chr5 89971896 89972026 GPR98 chr5 9004094590041044 GPR98 chr5 90049479 90049578 GPR98 chr5 90087039 90087138 GPR98chr5 90106831 90106930 GPR98 chr18 6943213 6943312 LAMA1 chr18 69471616947295 LAMA1 chr18 6955351 6955464 LAMA1 chr18 6980519 6980636 LAMA1chr18 6983097 6983233 LAMA1 chr18 6985525 6985642 LAMA1 chr18 70320647032175 LAMA1 chr18 7080285 7080456 LAMA1 chr4 85642561 85642725 WDFY3chr4 85695972 85696134 WDFY3 chr8 77775531 77775630 ZFHX4 chr10 1687325016873416 CUBN chr10 16930415 16930565 CUBN chr10 16957870 16957969 CUBNchr10 16979723 16979822 CUBN chr10 17087038 17087137 CUBN chr10 1713018917130288 CUBN chr3 187440245 187440389 BCL6 chr3 187442728 187442866BCL6 chr3 187449498 187449597 BCL6 chr5 123982951 123983050 ZNF608 chr5123985296 123985395 ZNF608 chr8 2824183 2824282 CSMD1 chr8 28574792857653 CSMD1 chr8 3038631 3038736 CSMD1 chr8 3165895 3165994 CSMD1 chr84494995 4495094 CSMD1 chr9 13221370 13221499 MPDZ chr9 13224501 13224600MPDZ chr5 13716704 13716803 DNAH5 chr5 13737466 13737565 DNAH5 chr513766039 13766138 DNAH5 chr5 13770878 13770977 DNAH5 chr5 1386453413864633 DNAH5 chr5 13883032 13883131 DNAH5 chr5 13894758 13894930 DNAH5chr5 13920588 13920726 DNAH5 chr22 23610594 23610702 BCR chr6 152443540152443639 SYNE1 chr6 152651704 152651803 SYNE1 chr6 152683304 152683458SYNE1 chr6 152702294 152702393 SYNE1 chr6 152730693 152730844 SYNE1 chr582789317 82789416 VCAN chr5 82843789 82843903 VCAN chr5 8287582382875922 VCAN chrX 32509457 32509556 DMD chrX 32583801 32583900 DMD chrX32613873 32613993 DMD chrX 32662259 32662358 DMD chrX 32717291 32717390DMD chrX 33146223 33146322 DMD chr3 164700030 164700198 SI chr3164700764 164700863 SI chr3 164735548 164735661 SI chr3 164776750164776870 SI chr3 164786865 164786983 SI chr7 48337962 48338084 ABCA13chr7 48547445 48547544 ABCA13 chr7 48550679 48550795 ABCA13 chr748559750 48559849 ABCA13 chr7 48682883 48682989 ABCA13 chr1 181452980181453079 CACNA1E chr1 181724372 181724533 CACNA1E chr1 181745236181745364 CACNA1E chr1 181759580 181759692 CACNA1E chr17 4046917140469270 STAT3 chr17 40474377 40474476 STAT3 chr17 40478125 40478224STAT3 chr17 40485908 40486067 STAT3 chr17 40491329 40491428 STAT3 chr1218534699 18534814 PIK3C2G chr12 18544055 18544186 PIK3C2G chr12 1857387118573970 PIK3C2G chr12 18699256 18699366 PIK3C2G chr12 18747415 18747514PIK3C2G chr2 169995086 169995216 LRP2 chr2 170010969 170011113 LRP2 chr2170012783 170012915 LRP2 chr2 170025048 170025186 LRP2 chr2 170101242170101341 LRP2 chr2 170115542 170115641 LRP2 chr19 6590080 6590179 CD70chr19 6590851 6591013 CD70 chr17 74011104 74011203 EVPL chr5 1111099411111093 CTNND2 chr5 11397142 11397315 CTNND2 chr5 11411647 11411764CTNND2 chr3 64547253 64547427 ADAMTS9 chr3 64579949 64580048 ADAMTS9chr18 60793423 60793599 BCL2-NA chr18 60774470 60774594 BCL2-NA chr8128764154 128764209 MYC-IGH chr14 106329109 106330460 IGH@-MYC chr3187461513 187463197 BCL6-NA chr11 69346747 69346916 CCND1-NA chr1860763905 60763963 BCL2-NA chr14 106323422 106328049 IGH@-MYC chr1860764357 60764467 BCL2-NA chr14 106239409 106242027 IGH@-BCL6 chr14106329407 106329468 IGHJ6 chr14 106330023 106330072 IGHJ5 chr14106330424 106330470 IGHJ4 chr14 106330796 106330845 IGHJ3 chr14106331408 106331460 IGHJ2 chr14 106331616 106331668 IGHJ1 chr14106494134 106494445 IGHV2-5.1 chr14 106494531 106494597 IGHV2-5.2 chr14106518399 106518704 IGHV3-7.1 chr14 106518807 106518932 IGHV3-7.2 chr14106725200 106725505 IGHV3-23.1 chr14 106725608 106725733 IGHV3-23.2chr14 106815721 106816026 IGHV3-33.1 chr14 106816127 106816253IGHV3-33.2 chr14 106829593 106829895 IGHV4-34.1 chr14 106829978106830076 IGHV4-34.2 chr14 106877618 106877926 IGHV4-39.1 chr14106878009 106878126 IGHV4-39.2 chr14 106993813 106994118 IGHV3-48.1chr14 106994221 106994346 IGHV3-48.2 chr14 107034728 107035033IGHV5-51.1 chr14 107035116 107035221 IGHV5-51.2 chr14 107169930107170235 IGHV1-69.1 chr14 107170321 107170428 IGHV1-69.2 chrX 100611039100611256 BTK

TABLE 9 Chromosome Start (bp) End (bp) chr17 7572917 7573017 chr177573926 7574033 chr17 7576510 7576691 chr17 7576839 7576939 chr177577018 7577155 chr17 7577498 7577608 chr17 7578176 7578289 chr177578361 7578554 chr17 7579310 7579590 chr17 7579660 7579760 chr177579825 7579925 chr17 8926070 8926201 chr17 10402290 10402409 chr1710416183 10416283 chr17 20799111 20799211 chr17 21319121 21319799 chr1726874644 26874744 chr17 26962100 26962200 chr17 27248705 27248826 chr1737879790 37879913 chr17 37880164 37880264 chr17 37880978 37881164 chr1737881301 37881457 chr17 37881567 37881667 chr17 37881959 37882106 chr1737882813 37882913 chr17 40556937 40557366 chr17 40837331 40837431 chr1744845791 44846006 chr17 51900603 51902313 chr17 56344761 56344861 chr1766938077 66938177 chr17 75208099 75208231 chr17 79414082 79414310 chr91056621 1056916 chr9 4118776 4118876 chr9 8404536 8404660 chr9 84852258485325 chr9 16419232 16419555 chr9 17761421 17761521 chr9 1877692918777149 chr9 21968184 21968284 chr9 21968697 21968797 chr9 2197090021971207 chr9 21974475 21974826 chr9 21994137 21994330 chr9 3497654634976646 chr9 78789901 78790045 chr9 94486757 94487239 chr9 104385599104385715 chr9 111617085 111618027 chr9 113538082 113538182 chr9115806435 115806535 chr9 121929789 121930084 chr9 126135805 126136023chr9 129957369 129957496 chr9 131479014 131479114 chr3 1424636 1424791chr3 9989136 9989306 chr3 10320050 10320150 chr3 18390797 18390897 chr326751364 26751574 chr3 36484921 36485092 chr3 38748769 38748874 chr338802762 38802862 chr3 38891989 38892089 chr3 41266057 41266157 chr348691721 48691885 chr3 49698932 49699032 chr3 52473987 52474098 chr354921982 54922082 chr3 55504230 55504574 chr3 64132763 64132863 chr365415276 65415406 chr3 66023701 66023853 chr3 73453378 73453543 chr374315631 74315800 chr3 77623656 77623874 chr3 78649348 78649459 chr379174597 79174697 chr3 81586069 81586169 chr3 89259079 89259477 chr389468390 89468530 chr3 93615419 93615535 chr3 96706192 96706776 chr3102196391 102196491 chr3 112991330 112991514 chr3 114069871 114070512chr3 119886475 119886856 chr3 120366691 120366791 chr3 126071038126071314 chr3 126736297 126736397 chr3 134920319 134920485 chr3142681457 142681817 chr3 147127985 147128783 chr3 154146591 154147111chr3 164712044 164712193 chr3 178935997 178936122 chr3 178951881178952152 chr3 180359871 180359971 chr3 186760514 186760878 chr1 47719724772524 chr1 10384028 10384128 chr1 10386319 10386419 chr1 1159162111591767 chr1 12266840 12266983 chr1 12785336 12785494 chr1 1613389516133995 chr1 16474992 16475531 chr1 17668793 17668897 chr1 2323450423234604 chr1 27087376 27087504 chr1 27100070 27100208 chr1 2722405227224180 chr1 27332657 27332876 chr1 33957160 33957271 chr1 3693706536937198 chr1 46826375 46826500 chr1 58946674 58946836 chr1 6714756967147934 chr1 70446049 70446149 chr1 70493913 70494013 chr1 8659123486591334 chr1 89734411 89734539 chr1 103345310 103345410 chr1 103364473103364573 chr1 103477947 103478047 chr1 103491355 103491508 chr1111216542 111216792 chr1 114340236 114340481 chr1 152286493 152287124chr1 154988884 154989109 chr1 155629489 155629589 chr1 156823777156823877 chr1 157514663 157514763 chr1 157804283 157804383 chr1158063128 158063236 chr1 158064475 158064857 chr1 158151970 158152070chr1 158224988 158225088 chr1 158324296 158324396 chr1 158325168158325273 chr1 158590011 158590111 chr1 158592793 158592957 chr1158609659 158609797 chr1 158626350 158626450 chr1 158637683 158637802chr1 159002313 159002481 chr1 159021500 159021857 chr1 161721457161721571 chr1 175087761 175087884 chr1 181731701 181731801 chr1183849784 183849884 chr1 185891510 185891631 chr1 185902883 185902983chr1 185958619 185958779 chr1 186008859 186009011 chr1 186043872186044023 chr1 186105987 186106087 chr1 190067507 190067651 chr1193028302 193028402 chr1 196227370 196227470 chr1 196642119 196642271chr1 204518477 204518600 chr1 210977341 210977489 chr1 211093048211093383 chr1 215972269 215972459 chr1 216419942 216420152 chr1231935853 231935956 chr1 232649895 232650139 chr1 237791187 237791287chr1 240555786 240555886 chr1 244640829 244640929 chr1 249212294249212442 chr18 6896500 6896625 chr18 9887970 9888070 chr18 3153740131537501 chr18 44560449 44560926 chr18 48591822 48591922 chr18 4859338848593557 chr18 55247286 55247431 chr18 59195225 59195330 chr18 6064263960642792 chr18 74963050 74963150 chr19 5244043 5244327 chr19 76872167687330 chr19 8609180 8609348 chr19 8808080 8808405 chr19 1113419311134307 chr19 21992321 21992491 chr19 22157365 22157560 chr19 2236420822364308 chr19 22942330 22942459 chr19 31039134 31040222 chr19 3584290935843009 chr19 37210102 37210288 chr19 37440440 37440631 chr19 3797508137975181 chr19 40408485 40408619 chr19 43698598 43698698 chr19 4644379946443899 chr19 47935612 47935712 chr19 49385288 49385460 chr19 5118949351189612 chr19 51330100 51330200 chr19 51645679 51645779 chr19 5227223352272763 chr19 52327729 52328003 chr19 52538241 52538341 chr19 5361256953612917 chr19 54310748 54310919 chr19 55593821 55593970 chr19 5581503455815194 chr19 57293320 57293489 chr19 58048608 58048914 chr19 5860131858601479 chr13 19751147 19751388 chr13 28014250 28014404 chr13 3274531732745417 chr13 33590917 33591017 chr13 36046536 36046673 chr13 3668605436686248 chr13 36748858 36749006 chr13 36886455 36886614 chr13 3688836836888468 chr13 37427676 37427776 chr13 38237609 38237780 chr13 4898565548985755 chr13 58206729 58208431 chr13 58298776 58299424 chr13 6687876266878862 chr13 73636053 73636410 chr13 74518112 74518212 chr13 7847729878477398 chr13 92345718 92345965 chr13 94482408 94482634 chr13 102047547102047710 chr13 114083266 114083407 chr16 9857863 9858061 chr16 1404183414041934 chr16 31926456 31926578 chr16 49430407 49430537 chr16 5117105451171343 chr16 55362612 55363185 chr16 55690614 55690714 chr16 6176099761761119 chr16 61851396 61851498 chr16 61891022 61891142 chr16 6193528761935387 chr16 65022116 65022246 chr16 66956016 66956116 chr16 6733332967333429 chr16 77228294 77228416 chr16 77353745 77353960 chr16 7776972477769883 chr16 80638284 80638443 chr16 80654659 80654832 chr16 8196986281969962 chr16 86544658 86544806 chr5 1221945 1222045 chr5 52399105240010 chr5 5319135 5319251 chr5 9190413 9190523 chr5 19473404 19473753chr5 23510028 23510136 chr5 23521131 23521288 chr5 24487934 24488034chr5 24491769 24491869 chr5 24505220 24505357 chr5 24537581 24537715chr5 26915768 26915868 chr5 32712169 32712504 chr5 35876342 35876442chr5 42719339 42719496 chr5 63256293 63257273 chr5 67576744 67576844chr5 67591054 67591154 chr5 83402530 83402630 chr5 100222166 100222266chr5 101592839 101592939 chr5 101593646 101593791 chr5 101834362101834478 chr5 109190864 109190964 chr5 128983457 128983588 chr5135692537 135692637 chr5 136324145 136324264 chr5 140182123 140182957chr5 140222588 140222721 chr5 140562893 140563039 chr5 140811004140811172 chr5 148407162 148407434 chr5 156346460 156346560 chr5161116251 161116351 chr5 169423079 169423179 chr5 172659657 172659843chr5 178413398 178413499 chr5 180661254 180661354 chr12 939168 939326chr12 4735912 4736043 chr12 6632065 6632165 chr12 7635238 7635358 chr129162021 9162133 chr12 9754096 9754196 chr12 9833518 9833629 chr1223696144 23696318 chr12 23728695 23728795 chr12 23893800 23893973 chr1225378548 25378707 chr12 25380167 25380346 chr12 25398207 25398318 chr1229614786 29614941 chr12 41900282 41900382 chr12 43944891 43944991 chr1245410193 45410293 chr12 52910577 52910677 chr12 55420590 55421030 chr1256647957 56648057 chr12 57553698 57553798 chr12 62786832 62786982 chr1263544118 63544218 chr12 75601408 75601619 chr12 85266927 85267027 chr1285450573 85450673 chr12 85517871 85517971 chr12 85531627 85531747 chr1286373752 86374125 chr12 89744605 89744705 chr12 94975667 94975767 chr1299640240 99640522 chr12 100704821 100704971 chr12 103352293 103352639chr12 104476511 104476611 chr12 106460715 106460815 chr12 108169109108169494 chr12 108985454 108985675 chr12 113704047 113704147 chr12117768680 117768780 chr12 118198821 118199091 chr12 118599728 118599828chr12 121972398 121972498 chr12 128899734 128899839 chr12 130184676130185148 chr2 271852 271952 chr2 1241659 1241789 chr2 1643073 1643194chr2 16736322 16736422 chr2 17830731 17830863 chr2 23985078 23985216chr2 27668162 27668316 chr2 31588841 31588975 chr2 31805700 31805800chr2 37873276 37873554 chr2 48808233 48809281 chr2 49217690 49217790chr2 50463971 50464075 chr2 51254718 51255389 chr2 60687822 60688673chr2 65540875 65541009 chr2 70188461 70188561 chr2 70903839 70903939chr2 70910754 70910854 chr2 71791187 71791343 chr2 80136815 80136915chr2 85622655 85622755 chr2 96689056 96689188 chr2 96780826 96781620chr2 100209964 100210093 chr2 100623671 100623845 chr2 107459958107460195 chr2 113310221 113310393 chr2 116497314 116497469 chr2116599786 116599921 chr2 125232315 125232456 chr2 125521553 125521724chr2 125555813 125555913 chr2 138169196 138169428 chr2 141031997141032097 chr2 141093237 141093339 chr2 141135748 141135855 chr2141356206 141356306 chr2 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118030440118030586 chr10 118236140 118236349 chr10 125804115 125804341 chr10134015427 134015645 chr11 688305 688471 chr11 5536639 5537070 chr116261526 6261826 chr11 6265171 6265623 chr11 7324276 7324605 chr1116007783 16007975 chr11 17757839 17757982 chr11 20959285 20959435 chr1127679603 27680129 chr11 35640938 35641197 chr11 36520053 36520129 chr1161607817 61607889 chr11 61607892 61607971 chr11 63137763 63137898 chr1163398538 63398691 chr11 64419483 64419650 chr11 64559608 64559755 chr1165349827 65349958 chr11 88911803 88911964 chr11 89916058 89916193 chr1192087030 92087280 chr11 92430500 92430642 chr11 92616180 92616319 chr11103907615 103908737 chr11 106680753 106681085 chr11 110450591 110451018chr11 113853816 113854026 chr11 117374573 117374714 chr11 120996248120996577 chr11 120998678 120999035 chr11 121000523 121000916 chr11121016583 121016805 chr11 124794740 124794986 chr11 125255420 125255637chr12 939134 939350 chr12 4735883 4736074 chr12 6632038 6632173 chr127635216 7635385 chr12 9161987 9162169 chr12 9754072 9754208 chr129833497 9833670 chr12 23696109 23696284 chr12 23728719 23728827 chr1223893769 23893994 chr12 25378518 25378628 chr12 25378668 25378736 chr1225380138 25380301 chr12 25380308 25380385 chr12 25398223 25398302 chr1229614758 29614988 chr12 41900251 41900405 chr12 43944864 43945004 chr1245410164 45410312 chr12 52910577 52910693 chr12 55420557 55421064 chr1256647927 56648077 chr12 57553677 57553810 chr12 62786802 62787016 chr1263544092 63544240 chr12 75601385 75601647 chr12 85266893 85267046 chr1285450548 85450693 chr12 85517848 85517985 chr12 85531603 85531772 chr1286373723 86374150 chr12 89744581 89744720 chr12 94975641 94975790 chr1299640216 99640562 chr12 100704786 100704860 chr12 100704936 100705008chr12 103352259 103352658 chr12 104476489 104476628 chr12 106460689106460851 chr12 108169076 108169504 chr12 108985426 108985704 chr12113704019 113704099 chr12 113704104 113704186 chr12 117768645 117768792chr12 118198800 118199112 chr12 118599695 118599850 chr12 121972376121972515 chr12 128899739 128899867 chr12 130184644 130184753 chr12130184809 130184885 chr12 130184909 130185183 chr13 19751265 19751337chr13 28014224 28014388 chr13 32745291 32745431 chr13 33590892 33591034chr13 36046510 36046687 chr13 36686030 36686279 chr13 36748835 36749047chr13 36886430 36886636 chr13 36888345 36888480 chr13 37427655 37427803chr13 38237585 38237799 chr13 48985620 48985766 chr13 58206696 58208461chr13 58298751 58299440 chr13 66878731 66878891 chr13 73636031 73636448chr13 74518091 74518231 chr13 78477276 78477414 chr13 92345686 92345987chr13 94482380 94482674 chr13 102047515 102047743 chr13 114083234114083422 chr14 24534187 24534331 chr14 26917367 26917620 chr14 2691762226918013 chr14 29237015 29237623 chr14 42356512 42356900 chr14 4564462345644841 chr14 52507344 52507559 chr14 52534609 52534757 chr14 5993063059930772 chr14 60193815 60193959 chr14 65007927 65008064 chr14 6524194265242105 chr14 68052644 68052856 chr14 77275791 77276011 chr14 8894576488945951 chr14 90650455 90650744 chr14 94964307 94964766 chr14 100317221100317362 chr15 23810994 23812075 chr15 23812104 23812292 chr15 2393144923932333 chr15 25221988 25222205 chr15 25958993 25959413 chr15 2757200327572180 chr15 28358221 28358406 chr15 31851263 31851391 chr15 3395438933954851 chr15 48062677 48063833 chr15 49217039 49217144 chr15 4932768949327837 chr15 51350391 51350633 chr15 54586066 54586272 chr15 5936820559368361 chr15 70960066 70960430 chr15 83226495 83226647 chr15 8333253083332744 chr15 89861776 89861937 chr15 91548896 91549040 chr15 9245945692459611 chr16 9857831 9858081 chr16 14041800 14041955 chr16 3192642731926615 chr16 49430376 49430561 chr16 51171023 51171301 chr16 5117130351171376 chr16 55362580 55362656 chr16 55362685 55363221 chr16 5569058055690735 chr16 61760969 61761154 chr16 61851374 61851518 chr16 6189099061891168 chr16 61935255 61935413 chr16 65022085 65022275 chr16 6695598966956133 chr16 67333294 67333438 chr16 77228269 77228449 chr16 7735371977354000 chr16 77769694 77769909 chr16 80638256 80638472 chr16 8065463180654861 chr16 81969830 81969975 chr16 86544628 86544841 chr17 75728897573037 chr17 7573894 7574051 chr17 7576519 7576721 chr17 75768097576957 chr17 7576984 7577173 chr17 7577469 7577648 chr17 75781547578326 chr17 7578339 7578589 chr17 7579289 7579605 chr17 75796397579772 chr17 7579804 7579954 chr17 8926044 8926230 chr17 1040226510402434 chr17 10416150 10416306 chr17 21319098 21319177 chr17 2131917821319512 chr17 21319628 21319704 chr17 21319713 21319791 chr17 2687461126874770 chr17 26962076 26962218 chr17 27248681 27248859 chr17 3787976837879938 chr17 37880143 37880277 chr17 37880943 37881204 chr17 3788126837881488 chr17 37881538 37881678 chr17 37881938 37882156 chr17 3788278837882929 chr17 40556914 40557410 chr17 40837296 40837461 chr17 4484576344846043 chr17 51900576 51902327 chr17 56344734 56344879 chr17 6693804866938188 chr17 75208118 75208269 chr17 79414058 79414333 chr18 68964746896661 chr18 9887936 9888078 chr18 31537375 31537513 chr18 4456041944560951 chr18 48591799 48591943 chr18 48593364 48593571 chr18 5524726555247478 chr18 59195203 59195340 chr18 60642613 60642830 chr18 7496302974963171 chr19 5244021 5244366 chr19 7687191 7687359 chr19 86091568609380 chr19 8808048 8808442 chr19 11134169 11134342 chr19 2199229021992423 chr19 21992445 21992515 chr19 22157475 22157578 chr19 2236418022364329 chr19 22942295 22942368 chr19 22942385 22942466 chr19 3103911131040259 chr19 35842887 35843029 chr19 37210077 37210321 chr19 3744041237440659 chr19 37975047 37975200 chr19 40408494 40408566 chr19 4369862043698690 chr19 46443765 46443918 chr19 47935615 47935688 chr19 4938526549385472 chr19 51189464 51189643 chr19 51330069 51330210 chr19 5164565651645811 chr19 52272206 52272803 chr19 52327701 52328023 chr19 5253821652538362 chr19 53612546 53612935 chr19 54310713 54310933 chr19 5559379855593996 chr19 55815008 55815221 chr19 57293289 57293504 chr19 5804857558048946 chr19 58601285 58601365 chr19 58601390 58601493 chr20 19610101961155 chr20 2375810 2375956 chr20 9524993 9525161 chr20 1190405011904305 chr20 21494090 21494308 chr20 21687095 21687423 chr20 2169536021695505 chr20 23016277 23016591 chr20 23807162 23807243 chr20 2546258125462771 chr20 30584543 30584934 chr20 34022178 34022541 chr20 3506015035061028 chr20 39831656 39831808 chr20 41419816 41420067 chr20 4468036144680513 chr20 44839061 44839205 chr20 44845441 44845647 chr20 5457891954579121 chr20 57035998 57036348 chr20 57042353 57042494 chr20 5782921657829641 chr20 60448762 60448971 chr20 60887662 60887869 chr20 6154240961542627 chr20 62045419 62045559 chr20 62121839 62122023 chr21 1090693110907002 chr21 15872914 15873066 chr21 22849657 22849793 chr21 2829651328296924 chr21 28327036 28327210 chr21 31538276 31538876 chr21 3263860032639172 chr21 36206693 36206849 chr21 38302583 38302718 chr21 4141442441414618 chr22 17072700 17073132 chr22 18028270 18028588 chr22 2289246722892686 chr22 24583982 24584137 chr22 26423336 26423552 chr22 3812150738121820 chr22 39626067 39626279 chr22 40140083 40140243 chr22 4107694041077923 chr22 45281706 45281848 chr22 46318804 46318959 chr22 4632697446327113 chr22 50302869 50303006 chrX 62875334 62875489

TABLE 10 chr16 3786650 3786816 CREBBP chr16 3788559 3788673 CREBBP chr933798483 33798620 PRSS3 chr7 148508714 148508813 EZH2 chr22 2323023223230432 IGLL5 chr18 60985291 60985897 BCL2 chr12 57496608 57496707STAT6 chr7 2979473 2979572 CARD11 chr6 27114203 27114486 HIST1H2BK chr933796640 33796800 PRSS3 chr1 39322631 39322730 RRAGC chr1 24912612491417 TNFRSF14 chr17 62006585 62006684 CD79B chr12 49415825 49415934MLL2 chrX 150573387 150573530 VMA21 chr1 150727476 150727626 CTSS chr933798014 33798113 PRSS3 chr6 26156786 26157248 HIST1H1E chr20 1763966717640053 RRBP1 chr1 2492058 2492157 TNFRSF14 chr12 49424675 49424816MLL2 chr12 49433246 49433389 MLL2 chrX 153663644 153663743 ATP6AP1 chr820074730 20074835 ATP6V1B2 chr18 9887338 9887437 TXNDC2 chr16 5798325057983349 CNGB1 chr22 41565506 41565620 EP300 chr2 119604215 119604314EN1 chr3 183273198 183273297 KLHL6 chr7 142131525 142131624 TRBV5-6 chr4146695657 146695824 ZNF827 chr19 19260016 19260115 MEF2B chr20 4852210848522207 SPATA2 chr2 51254638 51254737 NRXN1 chr10 94452434 94452533HHEX chr1 150470131 150470230 TARS2 chr19 50861848 50861947 NAPSA chr1955903047 55903146 RPL28 chr5 149792187 149792312 CD74 chr6 2612457726124741 HIST1H2AC chr9 1056514 1056613 DMRT2 chr1 2489781 2489907TNFRSF14 chr1 2493111 2493254 TNFRSF14 chr17 48823117 48823216 LUC7L3chr1 52933846 52933945 ZCCHC11 chr12 49440435 49440534 MLL2 chr626234697 26234796 HIST1H1D chr3 42787414 42787519 CCDC13 chr7 121653384121653483 PTPRZ1 chr16 1823023 1823122 MRPS34 chr12 92539163 92539311BTG1 chr3 141162243 141162342 ZBTB38 chr10 90773888 90774026 FAS chr840011192 40011291 C8orf4 chr6 26123881 26123980 HIST1H2BC chr12113496061 113496212 DTX1 chr2 43452587 43452686 ZFP36L2 chr5 140176763140176862 PCDHA2 chr6 37138342 37138441 PIM1 chr11 86133615 86133757CCDC81 chr7 87912060 87912159 STEAP4 chr2 182413251 182413350 CERKL chr632906520 32906619 HLA-DMB chr12 39756899 39757015 KIF21A chr15 4581443545814534 SLC30A4 chr15 42147520 42147619 SPTBN5 chr9 33799025 33799178PRSS3 chr6 132270569 132270668 CTGF chr2 232660773 232660872 COPS7Bchr10 101147908 101148058 CNNM1 chr17 5036195 5036294 USP6 chr6160953562 160953681 LPA chr1 160182899 160183055 PEA15 chrX 119388935119389034 ZBTB33 chr14 51237122 51237221 NIN chr1 78401606 78401705 NEXNchr7 27204662 27204761 HOXA9 chr16 85954780 85954882 IRF8 chr16 1988373019883829 GPRC5B chr20 39991558 39991657 EMILIN3 chr9 90260809 90260929DAPK1 chr9 34658516 34658680 IL11RA chr12 49425799 49426352 MLL2 chr2055840785 55840987 BMP7 chr6 27835005 27835210 HIST1H1B chr2 240981542240982132 PRR21 chr19 22155090 22155726 ZNF208 chr4 1388358 1388594CRIPAK chr12 11461506 11461743 PRB4 chr12 11214190 11214473 TAS2R46 chr3147108721 147109023 ZIC4 chr7 48312016 48312587 ABCA13 chr12 4943094349432739 MLL2 chr12 49420059 49420689 MLL2 chr1 203274734 203274876 BTG2chr22 41566409 41566575 EP300 chr4 126242585 126242703 FAT4 chr1127384450 27384549 CCDC34 chr19 10335462 10335561 S1PR2 chr4 3879949438799593 TLR1 chr6 136594219 136594325 BCLAF1 chr22 29885560 29885659NEFH chr10 70547910 70548021 CCAR1 chr13 33716428 33716527 STARD13 chrX142718477 142718576 SLITRK4 chr20 23615890 23616004 CST3 chr7 138969220138969319 UBN2 chr1 21808089 21808262 NBPF3 chr16 28603746 28603845SULT1A2 chr2 166872116 166872237 SCN1A chr1 214170811 214170950 PROX1chr21 35189750 35189849 ITSN1 chr16 3781275 3781374 CREBBP chr8113484819 113484936 CSMD3 chr17 61775911 61776071 LIMD2 chr12 1864438418644492 PIK3C2G chr8 48736418 48736557 PRKDC chr9 133957445 133957548LAMC3 chrX 125955251 125955356 CXorf64 chr14 50246930 50247040 KLHDC2chrX 21450701 21450800 CNKSR2 chr17 45214636 45214735 CDC27 chr417706617 17706716 FAM184B chr3 75787081 75787180 ZNF717 chr9 130742270130742416 FAM102A chr1 171123267 171123366 FMO6P chr1 21031259 21031369KIF17 chr2 96617076 96617175 ANKRD36C chr4 148589689 148589796 PRMT10chr2 160239061 160239160 BAZ2B chr16 1279269 1279439 TPSB2 chr1 4608700646087105 CCDC17 chr8 52733109 52733270 PCMTD1 chr6 26045849 26045948HIST1H3C chr1 2489164 2489273 TNFRSF14 chr6 168377012 168377111 HGC6.3chr10 129901079 129901178 MKI67 chr17 7578458 7578557 TP53 chr1285521621 85521720 LRRIQ1 chr9 139753456 139753584 MAMDC4 chr14 8032773880327837 NRXN3 chr1 149883474 149883573 SV2A chrX 32663176 32663275 DMDchr22 26829629 26829728 ASPHD2 chr19 35828674 35828773 CD22 chr1249416398 49416497 MLL2 chr12 49427855 49427954 MLL2 chr12 4943741749437565 MLL2 chr12 49439847 49439957 MLL2 chr12 49444221 49444346 MLL2chr12 49446989 49447104 MLL2 chr6 110714271 110714393 DDO chrX 2341099223411091 PTCHD1 chr7 299761 299860 FAM20C chr1 85733436 85733535 BCL10chr6 27861455 27861569 HIST1H2BO chr7 13935512 13935611 ETV1 chr770231146 70231245 AUTS2 chr17 79479257 79479380 ACTG1 chr18 4085410240854201 SYT4 chr2 114691855 114691963 ACTR3 chr14 47426601 47426752MDGA2 chr3 50293623 50293752 GNAI2 chr7 2977540 2977666 CARD11 chr11118343589 118343688 MLL chr1 10689828 10689937 PEX14 chr11 111249844111249943 POU2AF1 chr9 91965694 91965793 SECISBP2 chr17 4301171843011817 KIF18B chr3 64536567 64536738 ADAMTS9 chr1 111957481 111957580OVGP1 chr17 18145198 18145313 LLGL1 chr19 13054633 13054732 CALR chr629911909 29912008 HLA-A chrX 153663458 153663557 ATP6AP1 chr1 158913594158913693 PYHIN1 chr5 158141107 158141206 EBF1 chr1 228475527 228475626OBSCN chr3 9594028 9594127 LHFPL4 chr8 2910008 2910136 CSMD1 chr112337499 12337598 VPS13D chr6 41903681 41903780 CCND3 chr1 150443929150444028 RPRD2 chr6 74229045 74229144 EEF1A1 chr6 128298067 128298199PTPRK chr8 20073915 20074014 ATP6V1B2 chr10 97101320 97101435 SORBS1chr4 155505491 155505598 FGA chr12 104379380 104379506 TDG chr1211506386 11506485 PRB1 chr19 15132617 15132731 CCDC105 chr8 145024683145024815 PLEC chr16 67911411 67911559 EDC4 chr11 66639494 66639630 PCchr6 165711465 165711590 C6orf118 chrX 79932311 79932457 BRWD3 chr1554586092 54586262 UNC13C chr12 108954825 108954924 SART3 chr20 2963153329631632 FRG1B chr12 57905480 57905651 MARS chr21 43256219 43256318PRDM15 chr6 170627609 170627708 FAM120B chr8 8750154 8750253 MFHAS1 chr1240370922 240371023 FMN2 chr1 214818796 214818895 CENPF chr22 3742530037425399 MPST chr10 51465512 51465691 AGAP7 chr12 46244635 46244816ARID2 chr1 68512352 68512761 DIRAS3 chrX 7811644 7811830 VCX chr7127894552 127894740 LEP chr4 189012637 189012828 TRIML2 chr20 4372633243726529 KCNS1 chr5 140605138 140605339 PCDHB14 chr6 78172235 78173066HTR1B chr18 30350002 30350211 KLHL14 chrX 152244293 152244510 PNMA6Dchr12 11286598 11286821 TAS2R30 chr1 31194363 31194587 MATN1 chr4187524361 187524591 FAT1 chr17 63010386 63010623 GNA13 chr19 5054924950549492 ZNF473 chr14 104643890 104644134 KIF26A chr16 1306816 1307061TPSD1 chr7 151945006 151945257 MLL3 chrX 27839561 27839820 MAGEB10 chr2223523223 23523841 BCR chr17 57290162 57290449 SMG8 chr6 2605611226056422 HIST1H1C chr14 86088478 86088869 FLRT2 chr9 42410027 42410426ANKRD20A2 chr17 16612377 16612838 CCDC144A chr14 33292488 33292963 AKAP6chr6 1390621 1391103 FOXF2 chr11 85436759 85437249 SYTL2 chr1 245027101245027593 HNRNPU chr13 41239782 41240281 FOXO1 chr5 150945512 150946027FAT2 chr1 201178875 201180218 IGFN1 chr12 49433523 49434895 MLL2 chrX140993858 140995691 MAGEC1 chr11 70332042 70332575 SHANK2 chr2 5525251055253083 RTN4 chr19 16687146 16687737 MED26 chrX 125298695 125299314DCAF12L2 chr7 82582905 82583627 PCLO chr18 65180943 65181675 DSEL chr55461354 5462093 KIAA0947 chr3 40528745 40529496 ZNF619 chr1 249141669249142463 ZNF672 chr2 136872540 136873336 CXCR4 chr1 24201100 24201996CNR2 chr11 6567173 6568114 DNHD1 chr16 89350182 89351139 ANKRD11 chr1249422855 49422954 MLL2 chr12 49428357 49428456 MLL2 chr12 4942859449428718 MLL2 chr12 49433004 49433141 MLL2 chr12 49435961 49436060 MLL2chr12 49438185 49438305 MLL2 chr12 49440042 49440207 MLL2 chr12 4944174749441852 MLL2 chr12 49447258 49447424 MLL2 chr12 49448260 49448359 MLL2chr16 3786036 3786204 CREBBP chr16 3808854 3808973 CREBBP chr16 38177943817893 CREBBP chr16 3819151 3819250 CREBBP chr16 3828011 3828183 CREBBPchr16 3830732 3830879 CREBBP chr16 3900823 3900922 CREBBP chr9 3379478033794879 PRSS3 chr1 2488088 2488187 TNFRSF14 chr22 23235876 23235998IGLL5 chr22 23237632 23237731 IGLL5 chr1 16893673 16893846 NBPF1 chr116910088 16910191 NBPF1 chr1 16918406 16918505 NBPF1 chr2 9661426196614360 ANKRD36C chr1 145299788 145299887 NBPF10 chr1 145302725145302824 NBPF10 chr1 145314191 145314290 NBPF10 chr1 145323629145323728 NBPF10 chr1 145336256 145336355 NBPF10 chr1 145368413145368512 NBPF10 chr1 148010883 148011056 NBPF14 chr1 148013295148013394 NBPF14 chr1 148017501 148017665 NBPF14 chr1 148021552148021651 NBPF14 chr1 148025746 148025845 NBPF14 chr7 148506392148506491 EZH2 chr7 151836759 151836876 MLL3 chr7 151859918 151860017MLL3 chr7 151878655 151878754 MLL3 chr12 57493776 57493875 STAT6 chr1257498246 57498369 STAT6 chr12 57499029 57499128 STAT6 chr1 146406508146406607 NBPF12 chr1 146436711 146436810 NBPF12 chr1 146448373146448546 NBPF12 chr1 146457897 146458070 NBPF12 chr8 3047432 3047531CSMD1 chr8 3081250 3081389 CSMD1 chr18 60793423 60793599 BCL2-NA chr1860774470 60774594 BCL2-NA chr18 60763905 60763963 BCL2-NA chr18 6076435760764467 BCL2-NA chr14 107169930 107170235 IGHV1-69.1 chr14 107170321107170428 IGHV1-69.2 chr14 106610312 106610623 IGHV3-15.1 chr14106610726 106610852 IGHV3-15.2 chr14 106691672 106691977 IGHV3-21.1chr14 106692078 106692203 IGHV3-21.2 chr14 106725200 106725505IGHV3-23.1 chr14 106725608 106725733 IGHV3-23.2 chr14 106791004106791309 IGHV3-30.1 chr14 106791410 106791536 IGHV3-30.2 chr14106993813 106994118 IGHV3-48.1 chr14 106994221 106994346 IGHV3-48.2chr14 107218675 107218980 IGHV3-74.1 chr14 107219083 107219365IGHV3-74.2 chr14 106829593 106829895 IGHV4-34.1 chr14 106829978106830076 IGHV4-34.2 chr14 106877618 106877926 IGHV4-39.1 chr14106878009 106878126 IGHV4-39.2 chr14 106329407 106329468 IGHJ6 chr14106330023 106330072 IGHJ5 chr14 106330424 106330470 IGHJ4 chr14106330796 106330845 IGHJ3 chr14 106331408 106331460 IGHJ2 chr14106331616 106331668 IGHJ1

TABLE 11 Chromosome Start (bp) End (bp) Gene chr17 7578383 7578554 TP53chr17 7577018 7577155 TP53 chr17 7578176 7578289 TP53 chr9 2197101621971199 CDKN2A chr17 7577498 7577608 TP53 chr3 178935997 178936122PIK3CA chr9 21970899 21971199 CDKN2A chr20 29628226 29628331 FRG1B chr177579311 7579580 TP53 chr2 178098803 178098974 NFE2L2 chr20 2962587229625984 FRG1B chr9 139412203 139412382 NOTCH1 chr1 145302645 145302744NBPF10 chr3 178951963 178952086 PIK3CA chr9 20414286 20414385 MLLT3 chr4153247174 153247380 FBXW7 chr11 534211 534322 HRAS chr17 7576839 7576938TP53 chr1 145367713 145367822 NBPF10 chr19 40367823 40367922 FCGBP chr629910600 29910699 HLA-A chr7 86394555 86394735 GRM3 chr5 2451143524511616 CDH10 chr8 107782022 107782216 ABRA chr1 27100070 27100208ARID1A chr17 26684313 26684473 POLDIP2 chr2 141359045 141359175 LRP1Bchr16 72188111 72188258 PMFBP1 chr9 139402683 139402837 NOTCH1 chr3157146110 157146277 VEPH1 chr12 124798915 124799014 FAM101A chrX79999593 79999692 BRWD3 chr18 14542736 14543021 POTEC chr16 6503252165032725 CDH11 chr14 19553544 19553820 POTEG chr12 81471975 81472120ACSS3 chr7 55209978 55210130 EGFR chr6 119337959 119338094 FAM184A chr6152763209 152763380 SYNE1 chrX 79281102 79281201 TBX22 chr3 109049450109049549 DPPA4 chr7 111368415 111368577 DOCK4 chr22 22127161 22127271MAPK1 chr14 62547692 62547864 SYT16 chr1 16464346 16464479 EPHA2 chr1620442541 20442643 ACSM5 chr16 10995894 10996041 CIITA chr16 6498470964984857 CDH11 chr9 37014992 37015149 PAX5 chr6 31975095 31975194CYP21A2 chr9 139418202 139418374 NOTCH1 chr7 53103444 53104150 POM121L12chr6 27839691 27840063 HIST1H3I chr5 89943366 89943472 GPR98 chr2125192072 125192237 CNTNAP5 chr14 69701455 69701571 EXD2 chr3 181430808181430907 SOX2 chr7 6426828 6426927 RAC1 chr22 41652714 41652828 RANGAP1chr6 123869598 123869757 TRDN chr12 113515302 113515401 DTX1 chr201961099 1961356 PDYN chr1 217955515 217955664 SPATA17 chr19 2401029324010549 RPSAP58 chr9 21974671 21974770 CDKN2A chr2 202131209 202131506CASP8 chr11 40135933 40137642 LRRC4C chr2 80529766 80530938 LRRTM1 chr3178916835 178916947 PIK3CA chr16 75512868 75513584 CHST6 chr19 2215403822157389 ZNF208 chr8 139163586 139165359 FAM135B chr6 26204884 26205157HIST1H4E chr12 11545925 11546908 PRB2 chr5 63256300 63257092 HTR1A chr7154561126 154561281 DPP6 chr7 95157419 95157521 ASB4 chr6 5751258957512692 PRIM2 chr8 113585729 113585886 CSMD3 chr4 147560456 147560568POU4F2 chr1 145368535 145368634 NBPF10 chr7 37955723 37956081 SFRP4chr13 88327767 88330089 SLITRK5 chr4 187539226 187542862 FAT1 chr627834626 27835171 HIST1H1B chr5 140261864 140264052 PCDHA13 chr666204658 66205125 EYS chr1 57257787 57258100 C1orf168 chr7 2163952321639717 DNAH11 chr18 5397092 5397423 EPB41L3 chr2 202149545 202150040CASP8 chr1 157555965 157556231 FCRL4 chr5 24537560 24537765 CDH10 chr873848741 73850116 KCNB2 chr1 197390340 197391060 CRB1 chr18 1388463213885468 MC2R chr4 187627773 187630693 FAT1 chr5 26885756 26885968 CDH9chr7 88962839 88966280 ZNF804B chr5 140175844 140176834 PCDHA2 chr1930934638 30936599 ZNF536 chrX 140993367 140996183 MAGEC1 chr19 2072746220728771 ZNF737 chr8 88885017 88886181 DCAF4L2 chr15 23811026 23812218MKRN3 chr4 134071331 134073620 PCDH10 chr12 7636016 7636248 CD163 chr711675952 11676535 THSD7A chr6 96651055 96652002 FUT9 chr10 8474520684745340 NRG3 chr1 248028042 248028156 TRIM58 chr3 30691783 30691948TGFBR2 chr3 183756306 183756405 HTR3D chr1 198713182 198713332 PTPRCchr14 52520338 52520463 NID2 chr15 26806217 26806316 GABRB3 chr8139601514 139601677 COL22A1 chr1 176738745 176738864 PAPPA2 chr2138414365 138414539 THSD7B chr2 209308081 209308255 PTH2R chr8 113256622113256747 CSMD3 chr8 114110998 114111145 CSMD3 chr7 11630120 11630219THSD7A chr16 20570578 20570738 ACSM2B chr7 142459655 142459790 PRSS1chr11 132016188 132016287 NTM chr5 176709465 176709582 NSD1 chr1055955479 55955595 PCDH15 chr5 11082807 11082958 CTNND2 chr19 5446645254466611 CACNG8 chr1 104115728 104115870 AMY2B chr5 13719087 13719207DNAH5 chr14 47504313 47504489 MDGA2 chr1 75072309 75072554 C1orf173chr17 21318730 21319867 KCNJ12 chr5 23522737 23522988 PRDM9 chr734118610 34118795 BMPER chr13 36700036 36700223 DCLK1 chr5 140236299140237322 PCDHA10 chrX 37026543 37029321 FAM47C chr4 96761393 96762283PDHA2 chr3 147113699 147114230 ZIC4 chr18 64172066 64172406 CDH19 chr5140214168 140216301 PCDHA7 chrX 74494188 74494382 UPRT chr17 8078894380790329 ZNF750 chr14 44973722 44976121 FSCB chr19 57174960 57176561ZNF835 chr1 240370333 240371753 FMN2 chr1 216850475 216850747 ESRRG chr215415719 15415924 NBAS chr19 52618923 52620045 ZNF616 chr5 2352639423527863 PRDM9 chr5 140180883 140182957 PCDHA3 chr19 22940344 22941816ZNF99 chr12 4479555 4479838 FGF23 chr14 23346446 23346654 LRP10 chr1943268169 43268378 PSG8 chr19 54677829 54678114 MBOAT7 chr12 5748498557485458 NAB2 chr19 22940344 22942465 ZNF99 chr10 135438780 135438991FRG2B chr18 63547682 63547974 CDH7 chr3 169540213 169540508 LRRIQ4 chr1358206801 58208988 PCDH17 chr5 45262057 45262790 HCN1 chr7 121943817121944308 FEZF1 chr19 10610148 10610643 KEAP1 chr12 11420457 11421069PRB3 chr13 108518048 108518794 FAM155A chr22 37603210 37603433 SSTR3chr9 119976687 119976992 ASTN2 chrX 34960983 34962806 FAM47B chr6116937940 116938344 RSPH4A chr5 140552560 140554406 PCDHB7 chr9112898500 112900136 PALM2-AKAP2 chr19 5455842 5456254 ZNRF4 chr1813826242 13826657 MC5R chr3 155199247 155200710 PLCH1 chr7 6367973263680528 ZNF735 chr3 148458872 148459825 AGTR1 chr15 23889143 23890841MAGEL2 chr5 140474515 140476703 PCDHB2 chrX 142795188 142795519 SPANXN2chr1 190067523 190068200 FAM5C chr8 145770918 145771163 ARHGAP39 chr1205038983 205039124 CNTN2 chr2 141081461 141081635 LRP1B chr3 132435600132435753 NPHP3 chr3 109026902 109027050 DPPA2 chr6 119341141 119341266FAM184A chr1 205779409 205779509 SLC41A1 chr20 33033160 33033259 ITCHchr18 64178804 64178922 CDH19 chr6 129714206 129714305 LAMA2 chr1939103250 39103382 MAP4K1 chr2 200173514 200173613 SATB2 chr11 4524114145241257 PRDM11 chr2 28634819 28634950 FOSL2 chr3 97439104 97439254EPHA6 chr14 105996001 105996100 TMEM121 chr3 30713558 30713699 TGFBR2chr15 43927924 43928024 CATSPER2 chr1 149905298 149905423 MTMR11 chr1736927373 36927506 PIP4K2B chr3 140167411 140167510 CLSTN2 chr17 1024878310248933 MYH13 chr10 33199173 33199272 ITGB1 chr19 55401000 55401099FCAR chr12 109972412 109972571 UBE3B chr2 160206240 160206346 BAZ2B chr3157820502 157820663 SHOX2 chr19 50370310 50370461 PNKP chr20 4451911944519289 NEURL2 chr2 79254932 79255059 REG3G chr1 196295846 196296019KCNT2 chr14 30046462 30046561 PRKD1 chr6 30297128 30297276 TRIM39 chr1240492638 240492768 FMN2 chr19 58991733 58991832 ZNF446 chr4 19578421957941 WHSC1 chr15 75641370 75641469 NEIL1 chr6 55113473 55113572HCRTR2 chr3 157920860 157921034 RSRC1 chr8 113249418 113249577 CSMD3chr8 113317028 113317139 CSMD3 chr8 113599294 113599464 CSMD3 chr250280442 50280583 NRXN1 chr13 32972545 32972675 BRCA2 chr18 909476909586 ADCYAP1 chr18 14513660 14513784 POTEC chr8 110509147 110509296PKHD1L1 chr3 168838843 168839000 MECOM chr4 187557302 187557401 FAT1chr17 10369589 10369733 MYH4 chr3 37048468 37048567 MLH1 chr5 167812223167812360 WWC1 chr10 131640391 131640490 EBF3 chr20 278639 278738 ZCCHC3chr10 131565150 131565249 MGMT chr1 155887289 155887463 KIAA0907 chr626216706 26216848 HIST1H2BG chr12 54396230 54396329 HOXC9 chr19 1808491818085017 KCNN1 chr20 20177277 20177408 C20orf26 chr9 120470840 120471007TLR4 chr12 106708135 106708271 TCP11L2 chr15 84581961 84582060 ADAMTSL3chr9 139405104 139405257 NOTCH1 chr9 139412588 139412744 NOTCH1 chr9139413069 139413215 NOTCH1 chr4 79455602 79455769 FRAS1 chr5 4539663945396738 HCN1 chr19 17088177 17088350 CPAMD8 chr9 136234190 136234289SURF4 chr20 13839907 13840080 SEL1L2 chr11 122849988 122850144 BSX chr1250367086 50367244 AQP6 chr6 41165983 41166105 TREML2 chr4 6267951762679616 LPHN3 chr5 101834370 101834544 SLCO6A1 chr2 79349113 79349251REG1A chr17 7573926 7574033 TP53 chr2 217124225 217124379 MARCH4 chr1589424689 89424834 HAPLN3 chr16 77465304 77465454 ADAMTS18 chr5 1136485911364958 CTNND2 chr5 11397142 11397315 CTNND2 chr10 103826304 103826403HPS6 chr7 83610636 83610794 SEMA3A chr2 202151181 202151317 CASP8 chr1911470231 11470368 LPPR2 chr19 12951792 12951891 MAST1 chr10 108389024108389131 SORCS1 chr5 26903771 26903931 CDH9 chr2 1459847 1460008 TPOchr19 2121161 2121310 AP3D1 chrX 78616825 78616976 ITM2A chr6 6604491166045010 EYS chr5 13901431 13901564 DNAH5 chr19 37733482 37733581 ZNF383chr22 50654145 50654296 SELO chrX 12734345 12734914 FRMPD4 chr8 9297255392972728 RUNX1T1 chr1 161518210 161518385 FCGR3A chr2 164466119164467942 FIGN chr6 46107689 46108044 ENPP4 chr11 22301127 22301308 ANO5chr19 54313207 54314440 NLRP12 chr3 126707543 126708597 PLXNA1 chr373432745 73433987 PDZRN3 chr14 69256532 69257127 ZFP36L1 chr7 7241363372413897 POM121 chr3 147127953 147128847 ZIC1 chr1 186275518 186277191PRG4 chr11 30032399 30034074 KCNA4 chr4 9783798 9785062 DRD5 chr174506981 74507589 LRRIQ3 chr7 87913171 87913539 STEAP4 chr6 3202054332020731 TNXB chr15 23931583 23932342 NDN chrX 34148022 34150221 FAM47Achr6 26271336 26271610 HIST1H3G chr7 146829389 146829579 CNTNAP2 chr112887252 12887626 PRAMEF11 chr19 22362785 22364285 ZNF676 chr2 227924129227924320 COL4A4 chr10 68686714 68688016 LRRTM3 chrX 90690578 90691202PABPC5 chr5 11346513 11346705 CTNND2 chr22 32586994 32587271 RFPL2 chr1249420048 49420673 MLL2 chr12 130184385 130185157 TMEM132D chr7 5718757857188705 ZNF479 chr4 164393536 164394866 TKTL2 chr7 86415632 86416017GRM3 chr8 56015392 56015675 XKR4 chr20 50139751 50140541 NFATC2 chr256419678 56420320 CCDC85A chr15 48500014 48500300 SLC12A1 chr5 140589637140590605 PCDHB12 chr6 26158464 26158753 HIST1H2BD chr4 162306901162307559 FSTL5 chr17 10303757 10304049 MYH8 chr6 26225382 26225675HIST1H3E chr6 26056152 26056553 HIST1H1C chr3 113724487 113724692KIAA1407 chr19 55106643 55106849 LILRA1 chr4 110667390 110667596 CFIchr14 42355977 42357172 LRFN5 chr7 57528633 57529305 ZNF716 chr1953643670 53644867 ZNF347 chr12 78400291 78400964 NAV3 chr6 112671162112671571 RFPL4B chr6 87725250 87726087 HTR1E chr6 31323135 31323344HLA-B chr12 125397966 125398268 UBC chr1 111215825 111217040 KCNA3 chr3129695648 129695952 TRH chr1 38227190 38227732 EPHA10 chr1 1647512816475543 EPHA2 chr9 121929388 121930239 DBC1 chr5 140536956 140537263PCDHB17 chr12 130921488 130921798 RIMBP2 chr10 25886753 25887455 GPR158chr2 108626705 108626921 SLC5A7 chr22 40815101 40815317 MKL1 chr1149784912 149785128 HIST2H3D chrX 26212040 26212352 MAGEB6 chr2128338152 28338578 ADAMTS5 chr19 58384571 58386285 ZNF814 chr20 95610379561466 PAK7 chr4 52860789 52862055 LRRC66 chr1 148594406 148594625NBPF15 chr19 36357105 36357326 KIRREL2 chr3 197427592 197427813 KIAA0226chr19 55450491 55451378 NLRP7 chr17 7751610 7752329 KDM6B chr19 4605687446057096 OPA3 chr10 117884807 117885029 GFRA1 chr8 110980373 110980810KCNV1 chr1 214170118 214171410 PROX1 chr2 99012565 99013653 CNGA3 chr5140767491 140769260 PCDHGB4 chr9 104448968 104449193 GRIN3A chrX139865919 139866497 CDR1 chr12 11506213 11506796 PRB1 chr1 1318333413183781 LOC440563 chr11 5529367 5530481 UBQLN3 chr1 99771595 99772516LPPR4 chr10 29821794 29822127 SVIL chr19 21606157 21607280 ZNF493 chr627114237 27114572 HIST1H2BK chr6 146755039 146755795 GRM1 chr11 36808513681449 ART1 chr7 136699800 136700565 CHRM2 chr2 77745480 77746850LRRTM4 chr16 10273896 10274136 GRIN2A chr4 44176944 44177184 KCTD8 chr877775640 77775987 ZFHX4 chr1 151774039 151774827 LINGO4 chr18 79551217955364 PTPRM chr4 111397595 111398076 ENPEP chr15 85405870 85406115ALPK3 chr15 33954862 33955107 RYR3 chr14 47426599 47426844 MDGA2 chr1931038870 31040061 ZNF536 chr10 124339093 124339339 DMBT1 chr4 7007978870080273 UGT2B11 chrX 127185699 127185946 ACTRT1 chr1 215847663215848873 USH2A chr7 119914917 119915730 KCND2 chr2 11802095 11802346NTSR2 chrX 104463740 104464104 TEX13A chr6 134210543 134210908 TCF21chr4 187524461 187525111 FAT1 chr4 73012713 73012968 NPFFR2 chr731377951 31378781 NEUROD6 chr14 59112195 59113679 DACT1 chr10 5081919950820233 SLC18A3 chr12 78444554 78444927 NAV3 chr11 55032386 55032645TRIM48 chr9 116136230 116136606 HDHD3 chr5 38481935 38482197 LIFR chrX151869580 151870255 MAGEA6 chr11 18158850 18159706 MRGPRX3 chr1956952581 56954106 ZNF667 chr1 157557066 157557332 FCRL4 chr8 113697693113697959 CSMD3 chr2 106497875 106498398 NCK2 chr1 149859079 149859463HIST2H2AB chr9 140611226 140611610 EHMT1 chr22 17288659 17288927 XKR3chr1 176668317 176668585 PAPPA2 chr5 3599604 3600292 IRX1 chrX 125685233125686313 DCAF12L1 chr7 150325294 150325564 GIMAP6 chr14 7063359070634903 SLC8A3 chr2 51254660 51255051 NRXN1 chr17 29220392 29220784ATAD5 chr20 23016731 23017265 SSTR4 chr9 27949284 27950605 LINGO2 chr885799838 85800011 RALYL chr5 90040933 90041032 GPR98 chr2 3159825731598393 XDH chr22 41547849 41547948 EP300 chr22 41566409 41566575 EP300chr1 89616145 89616258 GBP7 chr4 57896470 57896569 POLR2B chr1 205034916205035037 CNTN2 chr6 50803913 50804012 TFAP2B chr16 61687869 61687980CDH8 chr6 105474260 105474359 LIN28B chr5 139192995 139193155 PSD2 chr2141027810 141027915 LRP1B chr2 141032077 141032176 LRP1B chr2 141299369141299475 LRP1B chr2 141457952 141458107 LRP1B chr2 141571225 141571375LRP1B chr2 141806555 141806673 LRP1B chr17 6665466 6665565 XAF1 chr1545003728 45003827 B2M chr22 37098522 37098621 CACNG2 chr3 186917475186917629 RTP1 chr1 89448464 89448606 RBMXL1 chr14 95033316 95033446SERPINA4 chr7 154172023 154172122 DPP6 chr10 87487702 87487801 GRID1chr3 109028016 109028177 DPPA2 chr9 104499619 104499718 GRIN3A chr1281503338 81503483 ACSS3 chr6 26027282 26027418 HIST1H4B chr7 3853064738530746 AMPH chr3 125879695 125879845 ALDH1L1 chr12 101510456 101510576ANO4 chr7 55221703 55221845 EGFR chr19 55106218 55106361 LILRA1 chr1955107219 55107318 LILRA1 chr6 152674397 152674568 SYNE1 chr6 152786397152786535 SYNE1 chr13 112722099 112722213 SOX1 chr19 22951999 22952126ZNF99 chr9 73164457 73164590 TRPM3 chr2 125281879 125282029 CNTNAP5 chr2125284861 125285033 CNTNAP5 chr5 36686233 36686332 SLC1A3

TABLE 12 Chromosome Start (bp) End (bp) chr12 22068691 22068802 chr1225378548 25378707 chr12 25380167 25380346 chr12 25398207 25398318 chr1278400210 78401193 chr17 7573926 7574033 chr17 7577018 7577155 chr177577498 7577608 chr17 7578176 7578289 chr17 7578361 7578461 chr177579311 7579546 chr17 26684313 26684473 chr17 37880164 37880264 chr1737880978 37881164 chr17 37881567 37881667 chr17 50008349 50008492 chr1751900393 51902406 chr2 21228063 21235357 chr2 29416090 29416788 chr229419631 29419731 chr2 29420408 29420542 chr2 29430037 29430138 chr229432648 29432748 chr2 29436849 29436949 chr2 29443572 29443701 chr229445192 29445292 chr2 29445378 29445478 chr2 29446207 29448431 chr240655642 40657411 chr2 77745476 77746913 chr2 79384697 79384824 chr279385451 79385589 chr2 80085138 80085305 chr2 80101224 80101466 chr280136739 80136923 chr2 80529444 80530834 chr2 107423137 107423369 chr2125261887 125262126 chr2 141242924 141243077 chr2 141665445 141665615chr2 155711287 155711820 chr2 167760006 167760383 chr2 168099081168108352 chr2 178098798 178098974 chr2 185800516 185803687 chr2228881145 228884872 chr2 237172849 237172949 chr3 41266080 41266180 chr373432629 73433920 chr3 147108740 147109014 chr3 147113642 147114236 chr3147127972 147128848 chr3 158983022 158983162 chr3 164905717 164908582chr3 178935997 178936122 chr3 178951881 178952152 chr7 18705889 18706013chr7 53103417 53104243 chr7 55241613 55241736 chr7 55242414 55242514chr7 55248985 55249171 chr7 55259411 55259567 chr7 55260446 55260546chr7 55266409 55266556 chr7 55268007 55268107 chr7 88962743 88966270chr7 100385559 100385717 chr7 116411902 116412043 chr7 116418829116419011 chr7 116423357 116423523 chr7 116435940 116436178 chr7119914694 119915701 chr7 126173011 126173905 chr7 136699632 136701008chr7 140453074 140453193 chr7 140481375 140481493 chr7 146829409146829584 chr19 1206998 1207145 chr19 1220371 1220504 chr19 12212111221339 chr19 10597327 10597494 chr19 10599867 10600044 chr19 1060032910600478 chr19 10602291 10602939 chr19 10610098 10610667 chr19 3093446830936638 chr19 31025753 31025906 chr19 31038885 31040384 chr19 3176748731770649 chr19 46627149 46627249 chr19 56538557 56539874 chr19 5732517157328936 chr8 10464569 10470796 chr8 19809301 19809452 chr8 5232066352322097 chr8 77616324 77618911 chr8 77690475 77690656 chr8 7776315577768514 chr8 88885085 88886198 chr8 113256632 113256798 chr8 113301593113301767 chr8 113304765 113304939 chr8 113569005 113569166 chr8113694669 113694858 chr8 113697651 113697959 chr8 133905936 133906139chr8 139151228 139151339 chr8 139163464 139165439 chr8 139606265139606411 chr5 15928015 15928580 chr5 19473457 19473820 chr5 2175186121752328 chr5 22078560 22078762 chr5 24487792 24488227 chr5 2450969824509911 chr5 24537494 24537781 chr5 26881252 26881725 chr5 2690606726906235 chr5 26915758 26916024 chr5 33576159 33577170 chr5 3368397533684142 chr5 33947278 33947473 chr5 45262059 45262891 chr5 4526719045267355 chr5 63256356 63257448 chr5 82937336 82937508 chr5 127648325127648487 chr5 161128506 161128739 chr5 168134980 168135126 chr657512507 57512692 chr6 117609655 117609965 chr6 117622137 117622300 chr6117629957 117630091 chr6 117631244 117631444 chr6 117632182 117632282chr6 117638306 117638435 chr6 117639333 117639433 chr6 117641031117658503 chr6 165715074 165715671 chr1 37271747 37271863 chr1 3734624637346445 chr1 46085194 46085368 chr1 74575077 74575237 chr1 7503685075039127 chr1 92185494 92185654 chr1 99771278 99772551 chr1 158627269158627431 chr1 158632505 158632685 chr1 167095142 167097827 chr1175086109 175086340 chr1 175372326 175372736 chr1 176563667 176564723chr1 176709118 176709331 chr1 176915086 176915253 chr1 177001609177001965 chr1 190028421 190029374 chr1 190067151 190068180 chr1190203501 190203607 chr1 196227361 196227562 chr1 237729889 237730050chr1 237777345 237778139 chr1 237886427 237886562 chr1 237947028237948233 chr1 247587188 247588870 chr1 248039226 248039722 chr921970900 21971204 chr9 119976663 119977014 chr9 120474685 120476922 chr446252333 46252605 chr4 48622655 48622795 chr4 96761310 96762445 chr4114274318 114280137 chr4 134071418 134073888 chr4 134084153 134084390chr4 153247224 153247368 chr4 164534470 164534652 chr11 3003231230034192 chr11 40136053 40137815 chr11 59828633 59828789 chr11 9253103092535026 chr11 113102894 113103059 chr11 132081915 132082041 chrX32429868 32430030 chrX 54497746 54497920 chrX 111195282 111195616 chrX112024167 112024328 chrX 125298529 125299762 chrX 125685235 125686525chrX 135426563 135432565 chrX 144904002 144906476 chr20 1961024 1961489chr20 9546563 9546971 chr20 57766243 57769791 chr10 25886709 25888201chr10 87628766 87628937 chr10 89692769 89693008 chr10 89711874 89712016chr10 89717609 89717776 chr14 42355848 42357213 chr14 99183496 99183609chr18 22804314 22807568 chr18 31322918 31326558 chr18 42529889 42533273chr18 43249311 43249421 chr13 58206760 58209200 chr13 70681341 70681820chr13 84453624 84455615 chr15 23810969 23812451 chr16 49669610 49672754chr16 51172603 51176051 chr21 44524418 44524518 track name =169393_3_NSCLC_(—) cfDNA_P1_tiled_region description = “169393_3_(—)NSCLC_cfDNA_P1_tiled_(—) region” chr1 37271737 37271884 chr1 3734622237346474 chr1 46085160 46085373 chr1 74575056 74575253 chr1 7503682375039145 chr1 92185462 92185681 chr1 99771257 99772585 chr1 158627234158627465 chr1 158632484 158632728 chr1 167095120 167095403 chr1167095405 167095612 chr1 167095615 167097864 chr1 175086076 175086211chr1 175086221 175086297 chr1 175372301 175372766 chr1 176563636176564760 chr1 176709091 176709343 chr1 176915056 176915285 chr1177001586 177002014 chr1 190028399 190029107 chr1 190029289 190029393chr1 190067124 190068224 chr1 190203479 190203619 chr1 196227340196227582 chr1 237729868 237730075 chr1 237777319 237778165 chr1237886394 237886578 chr1 237946994 237948243 chr1 247587162 247588364chr1 247588367 247588817 chr1 247588817 247588895 chr1 248039192248039764 chr2 21228028 21231818 chr2 21231828 21235395 chr2 2941606829416797 chr2 29419608 29419751 chr2 29420373 29420567 chr2 2943000329430159 chr2 29432613 29432759 chr2 29436818 29436971 chr2 2944353829443734 chr2 29445168 29445304 chr2 29445348 29445487 chr2 2944617829446811 chr2 29446818 29448463 chr2 40655614 40657081 chr2 4065708440657440 chr2 77745452 77746964 chr2 79384672 79384750 chr2 7938476279384840 chr2 79385437 79385543 chr2 80085113 80085323 chr2 8010119880101492 chr2 80136708 80136972 chr2 80529410 80530860 chr2 107423111107423415 chr2 125261856 125262161 chr2 141242897 141243115 chr2141665412 141665650 chr2 155711255 155711854 chr2 167759985 167760404chr2 168099060 168101401 chr2 168101415 168104825 chr2 168104835168105005 chr2 168105020 168108371 chr2 178098777 178098985 chr2185800493 185801915 chr2 185801923 185803706 chr2 228881110 228882939chr2 228882950 228884909 chr2 237172815 237172971 chr3 41266046 41266203chr3 73432596 73433959 chr3 147108706 147109059 chr3 147113616 147114276chr3 147127941 147128721 chr3 147128786 147128884 chr3 158982995158983175 chr3 164905695 164908611 chr3 178935989 178936159 chr3178951854 178952172 chr4 46252300 46252618 chr4 48622620 48622811 chr496761279 96762480 chr4 114274285 114280173 chr4 134071388 134073569 chr4134073573 134073927 chr4 134084118 134084405 chr4 153247190 153247405chr4 164534436 164534688 chr5 15927993 15928621 chr5 19473429 19473853chr5 21751830 21752361 chr5 22078530 22078784 chr5 24487765 24488112chr5 24488125 24488267 chr5 24509670 24509951 chr5 24537460 24537819chr5 26881229 26881749 chr5 26906034 26906250 chr5 26915729 26916048chr5 33576138 33577189 chr5 33683948 33684170 chr5 33947248 33947491chr5 45262028 45262905 chr5 45267168 45267374 chr5 63256324 63257490chr5 82937302 82937528 chr5 127648290 127648512 chr5 161128482 161128768chr5 168134946 168135167 chr6 57512480 57512720 chr6 117609633 117609983chr6 117622113 117622313 chr6 117629933 117630106 chr6 117631223117631463 chr6 117632148 117632288 chr6 117638273 117638470 chr6117639298 117639453 chr6 117641008 117641428 chr6 117641438 117642993chr6 117643003 117643174 chr6 117643188 117645141 chr6 117645158117646127 chr6 117646173 117647264 chr6 117647288 117648778 chr6117648783 117648918 chr6 117648943 117650620 chr6 117650623 117650824chr6 117650848 117651171 chr6 117651198 117651335 chr6 117651393117651470 chr6 117651563 117651698 chr6 117651783 117651861 chr6117652003 117652075 chr6 117652093 117652174 chr6 117652488 117652591chr6 117654168 117654233 chr6 117657138 117657333 chr6 117657883117658535 chr6 165715051 165715324 chr6 165715336 165715696 chr718705854 18706045 chr7 53103391 53104267 chr7 55241591 55241759 chr755242381 55242526 chr7 55248951 55249200 chr7 55259376 55259601 chr755260416 55260574 chr7 55266386 55266601 chr7 55267986 55268123 chr788962713 88966297 chr7 100385525 100385744 chr7 116411893 116412071 chr7116418808 116419051 chr7 116423323 116423536 chr7 116435918 116436202chr7 119914661 119915728 chr7 126172979 126173946 chr7 136699601136701045 chr7 140453047 140453121 chr7 140453152 140453225 chr7140481432 140481507 chr7 146829378 146829605 chr8 10464537 10465023 chr810465042 10465142 chr8 10465302 10465600 chr8 10465637 10465932 chr810465982 10466059 chr8 10466072 10469014 chr8 10469017 10470834 chr819809280 19809487 chr8 52320630 52322120 chr8 77616289 77618931 chr877690454 77690692 chr8 77763124 77765281 chr8 77765309 77766540 chr877766554 77768548 chr8 88885057 88885301 chr8 88885302 88885455 chr888885462 88885546 chr8 88885557 88886235 chr8 113256609 113256816 chr8113301569 113301781 chr8 113304734 113304955 chr8 113568979 113569192chr8 113694634 113694887 chr8 113697629 113697972 chr8 133905914133906161 chr8 139151207 139151354 chr8 139163432 139165467 chr8139606232 139606449 chr9 21970869 21971023 chr9 21971074 21971146 chr9119976629 119976988 chr9 120474664 120476938 chr10 25886682 25888217chr10 87628744 87628948 chr10 89692737 89692810 chr10 89692877 89692951chr10 89692972 89693037 chr10 89711887 89711966 chr10 89717577 89717711chr11 30032280 30033827 chr11 30033840 30034213 chr11 40136022 40137848chr11 59828607 59828816 chr11 92530995 92535065 chr11 113102866113103090 chr11 132081890 132082058 chr12 22068669 22068839 chr1225378518 25378628 chr12 25378668 25378736 chr12 25380138 25380301 chr1225380308 25380385 chr12 25398223 25398302 chr12 78400185 78400668 chr1278400730 78401224 chr13 58206731 58209217 chr13 70681316 70681856 chr1384453597 84455634 chr14 42355817 42357220 chr14 99183471 99183646 chr1523810934 23812061 chr15 23812104 23812496 chr16 49669576 49672771 chr1651172578 51173068 chr16 51173088 51174493 chr16 51174583 51174969 chr1651174978 51175198 chr16 51175198 51175317 chr16 51175353 51175532 chr1651175583 51175663 chr16 51175678 51175785 chr16 51175823 51175893 chr1651175943 51176086 chr17 7573894 7574051 chr17 7576984 7577173 chr177577469 7577648 chr17 7578154 7578326 chr17 7578339 7578478 chr177579289 7579578 chr17 26684291 26684509 chr17 37880143 37880277 chr1737880943 37881204 chr17 37881538 37881678 chr17 50008315 50008526 chr1751900371 51902443 chr18 22804281 22807572 chr18 31322885 31325872 chr1831325880 31326588 chr18 42529861 42533288 chr18 43249282 43249460 chr191206975 1207181 chr19 1220350 1220519 chr19 1221180 1221361 chr1910597293 10597511 chr19 10599833 10600090 chr19 10600303 10600514 chr1910602263 10602970 chr19 10610073 10610710 chr19 30934446 30936220 chr1930936236 30936655 chr19 31025721 31025947 chr19 31038856 31040406 chr1931767466 31769840 chr19 31769851 31770211 chr19 31770246 31770670 chr1946627115 46627261 chr19 56538529 56539902 chr19 57325149 57325572 chr1957325594 57325696 chr19 57325734 57328948 chr20 1960990 1961521 chr209546528 9546985 chr20 57766211 57769826 chr21 44524394 44524462 chr2144524464 44524541 chrX 32429836 32430057 chrX 54497725 54497925 chrX111195250 111195640 chrX 112024145 112024364 chrX 125298579 125298835chrX 125298859 125299288 chrX 125299329 125299403 chrX 125299449125299801 chrX 125685269 125685520 chrX 125685544 125685751 chrX125685759 125685831 chrX 125685854 125685972 chrX 125686009 125686084chrX 125686129 125686562 chrX 135426542 135432592 chrX 144903967144906492

TABLE 13 Chromosome Start (bp) Stop (bp) chr12 22068691 22068802 chr1225378548 25378707 chr12 25380167 25380346 chr12 25398207 25398318 chr1225479184 25479284 chr12 25549002 25549102 chr12 25619069 25619169 chr1225702320 25702420 chr12 49415559 49415659 chr12 49415825 49415934 chr1249416049 49416149 chr12 49416372 49416658 chr12 49418360 49418491 chr1249418592 49418729 chr12 49419964 49421105 chr12 49421585 49421713 chr1249421791 49421924 chr12 49422610 49422741 chr12 49422843 49423019 chr1249423171 49423271 chr12 49424062 49424222 chr12 49424383 49424551 chr1249424675 49424816 chr12 49424957 49427747 chr12 49427849 49428082 chr1249428176 49428276 chr12 49428357 49428457 chr12 49428594 49428718 chr1249430907 49432772 chr12 49433004 49433141 chr12 49433217 49433400 chr1249433506 49435318 chr12 49435413 49435513 chr12 49435686 49435786 chr1249435871 49436113 chr12 49436336 49436436 chr12 49436523 49436661 chr1249436858 49436969 chr12 49437128 49437228 chr12 49437417 49437565 chr1249437650 49437781 chr12 49437982 49438087 chr12 49438185 49438305 chr1249438526 49438748 chr12 49439676 49439776 chr12 49439847 49439957 chr1249440042 49440207 chr12 49440391 49440573 chr12 49441747 49441852 chr1249442441 49442552 chr12 49442887 49443001 chr12 49443464 49444573 chr1249444668 49446207 chr12 49446346 49446492 chr12 49446697 49446855 chr1249446989 49447104 chr12 49447258 49447424 chr12 49447760 49447923 chr1249448089 49448199 chr12 49448310 49448534 chr12 49448682 49448809 chr1249449033 49449133 chr12 69219731 69219831 chr12 69225913 69226013 chr1269233291 69233391 chr12 69240320 69240420 chr12 78400210 78401193 chr171011270 1011370 chr17 1028560 1028660 chr17 1059293 1059393 chr171083413 1083513 chr17 7572917 7573017 chr17 7573926 7574033 chr177576510 7576691 chr17 7576839 7576939 chr17 7577018 7577155 chr177577498 7577608 chr17 7578176 7578289 chr17 7578361 7578554 chr177579311 7579590 chr17 7579660 7579760 chr17 7579825 7579925 chr1726684313 26684473 chr17 37879790 37879913 chr17 37880164 37880264 chr1737880978 37881164 chr17 37881301 37881457 chr17 37881567 37881667 chr1737881959 37882106 chr17 37882813 37882913 chr17 50008349 50008492 chr1751900393 51902406 chr2 15760376 15760476 chr2 15804642 15804742 chr215908988 15909088 chr2 16012401 16012501 chr2 16082531 16082631 chr221228063 21235357 chr2 29416090 29416788 chr2 29419631 29419731 chr229420408 29420542 chr2 29430037 29430138 chr2 29432648 29432748 chr229436849 29436949 chr2 29443572 29443701 chr2 29445192 29445292 chr229445378 29445478 chr2 29446207 29448431 chr2 40655642 40657411 chr277745476 77746913 chr2 79384697 79384824 chr2 79385451 79385589 chr280085138 80085305 chr2 80101224 80101466 chr2 80136739 80136923 chr280529444 80530834 chr2 107423137 107423369 chr2 125261887 125262126 chr2125502734 125502919 chr2 141242924 141243077 chr2 141665445 141665615chr2 155711287 155711820 chr2 167760006 167760383 chr2 168099081168108352 chr2 178095513 178096736 chr2 178097120 178097290 chr2178097973 178098073 chr2 178098733 178098996 chr2 185800516 185803687chr2 198267280 198267550 chr2 212286730 212286830 chr2 212288879212289026 chr2 212293120 212293220 chr2 212295669 212295825 chr2212426627 212426813 chr2 212483901 212484001 chr2 212488646 212488769chr2 225338962 225339093 chr2 225342917 225343062 chr2 225346609225346795 chr2 225360549 225360683 chr2 225362468 225362568 chr2225365080 225365204 chr2 225367682 225367789 chr2 225368369 225368539chr2 225370673 225370849 chr2 225371575 225371720 chr2 225376071225376299 chr2 225378241 225378355 chr2 225379329 225379489 chr2225400245 225400358 chr2 225422376 225422573 chr2 225449644 225449744chr2 228881145 228884872 chr2 237172849 237172949 chr3 11635131 11635231chr3 11679363 11679463 chr3 11722418 11722518 chr3 11761268 11761368chr3 11806354 11806454 chr3 12626012 12626156 chr3 12632296 12632473chr3 12633199 12633299 chr3 38182623 38182777 chr3 41266080 41266180chr3 70303419 70303519 chr3 70586835 70586935 chr3 71015074 71015174chr3 71159348 71159448 chr3 71444358 71444458 chr3 73432629 73433920chr3 78286439 78286539 chr3 78766444 78766544 chr3 79472272 79472372chr3 80063205 80063305 chr3 80653452 80653552 chr3 81242598 81242698chr3 89259009 89259670 chr3 89390065 89390221 chr3 89390904 89391240chr3 147108740 147109014 chr3 147113642 147114236 chr3 147127972147128848 chr3 158983022 158983162 chr3 164905717 164908582 chr3168840391 168840491 chr3 169501256 169501356 chr3 169646255 169646355chr3 169896593 169896693 chr3 170140983 170141083 chr3 170716033170716133 chr3 178916538 178916965 chr3 178921331 178921577 chr3178927973 178928126 chr3 178935997 178936122 chr3 178951881 178952152chr3 181430148 181431102 chr3 182584093 182584193 chr3 182733240182733340 chr3 183014809 183014909 chr3 183273245 183273345 chr3183818306 183818406 chr3 189455528 189455657 chr3 189526060 189526315chr3 189586368 189586505 chr7 13894226 13894326 chr7 18705889 18706013chr7 53103417 53104243 chr7 54617645 54617745 chr7 55241613 55241736chr7 55242414 55242514 chr7 55248985 55249171 chr7 55259411 55259567chr7 55260446 55260546 chr7 55266409 55266556 chr7 55268007 55268107chr7 55492985 55493085 chr7 55750380 55750480 chr7 55990868 55990968chr7 57398678 57398928 chr7 88962743 88966270 chr7 100385559 100385717chr7 116411902 116412043 chr7 116417433 116417533 chr7 116418829116419011 chr7 116422041 116422151 chr7 116423357 116423523 chr7116435708 116435845 chr7 116435940 116436178 chr7 119914694 119915701chr7 126173011 126173905 chr7 136699632 136701008 chr7 140434396140434570 chr7 140439611 140439746 chr7 140449086 140449218 chr7140453074 140453193 chr7 140453960 140454060 chr7 140476711 140476888chr7 140477783 140477883 chr7 140481375 140481493 chr7 146133409146133607 chr7 146829409 146829584 chr7 152109145 152110115 chr191206912 1207202 chr19 1218407 1218507 chr19 1219317 1219417 chr191220371 1220504 chr19 1220579 1220716 chr19 1221211 1221339 chr191221926 1222026 chr19 1222983 1223171 chr19 1226452 1226646 chr194099198 4099412 chr19 4110506 4110653 chr19 4117416 4117627 chr1910597327 10597494 chr19 10599867 10600044 chr19 10600329 10600478 chr1910602291 10602939 chr19 10610098 10610667 chr19 30934468 30936638 chr1931025753 31025906 chr19 31038885 31040384 chr19 31767487 31770649 chr1946627149 46627249 chr19 56538557 56539874 chr19 57325171 57328936 chr82855569 2855669 chr8 3382862 3382962 chr8 4021464 4021564 chr8 46600664660166 chr8 5301234 5301334 chr8 5936810 5936910 chr8 10464569 10470796chr8 13733843 13733943 chr8 13959896 13959996 chr8 14338894 14338994chr8 14640268 14640368 chr8 14942769 14942869 chr8 15244538 15244638chr8 19809301 19809452 chr8 38173445 38173545 chr8 38179041 38179141chr8 38182800 38182900 chr8 38186559 38186659 chr8 38271435 38271541chr8 38271669 38271807 chr8 38272062 38272162 chr8 38272296 38272419chr8 38273387 38273578 chr8 38274823 38274934 chr8 38275387 38275509chr8 52320663 52322097 chr8 77616324 77618911 chr8 77690475 77690656chr8 77763155 77768514 chr8 88885085 88886198 chr8 113256632 113256798chr8 113301593 113301767 chr8 113304765 113304939 chr8 113569005113569166 chr8 113694669 113694858 chr8 113697651 113697959 chr8114668600 114668774 chr8 128360232 128360332 chr8 128377618 128377718chr8 128394799 128394899 chr8 128411949 128412049 chr8 128718569128718669 chr8 128750829 128750929 chr8 128766379 128766479 chr8128790280 128790380 chr8 129171307 129171407 chr8 129177137 129177237chr8 129181775 129181875 chr8 129187690 129187790 chr8 133905936133906139 chr8 139151228 139151339 chr8 139163464 139165439 chr8139606265 139606411 chr5 917120 917220 chr5 1034347 1034447 chr5 10839151084015 chr5 1216932 1217032 chr5 1295105 1295279 chr5 12091527 12091718chr5 15928015 15928580 chr5 19473457 19473820 chr5 21751861 21752328chr5 22078560 22078762 chr5 24487792 24488227 chr5 24509698 24509911chr5 24537494 24537781 chr5 26881252 26881725 chr5 26906067 26906235chr5 26915758 26916024 chr5 29809544 29809723 chr5 33576159 33577170chr5 33683975 33684142 chr5 33947278 33947473 chr5 36037958 36038058chr5 36183977 36184077 chr5 36679795 36679895 chr5 37370951 37371051chr5 38352315 38352415 chr5 39306756 39306856 chr5 45262059 45262891chr5 45267190 45267355 chr5 45292575 45292675 chr5 45321584 45321684chr5 45353227 45353327 chr5 63256356 63257448 chr5 82937336 82937508chr5 127648325 127648487 chr5 149498309 149498415 chr5 149499029149499129 chr5 149499574 149499686 chr5 149500450 149500573 chr5149500766 149500885 chr5 149501442 149501603 chr5 149502604 149502764chr5 149503812 149503923 chr5 149504289 149504394 chr5 149505007149505140 chr5 161128506 161128739 chr5 168134980 168135126 chr657512507 57512692 chr6 117609655 117609965 chr6 117622137 117622300 chr6117629957 117630091 chr6 117631244 117631444 chr6 117632182 117632282chr6 117638306 117638435 chr6 117639333 117639433 chr6 117641031117658503 chr6 161969910 161970010 chr6 162225660 162225760 chr6162490501 162490601 chr6 162753766 162753866 chr6 163149295 163149395chr6 165715074 165715671 chr1 37271747 37271863 chr1 37346246 37346445chr1 39927582 39927682 chr1 40035554 40035654 chr1 40124925 40125025chr1 40363293 40363393 chr1 40627140 40627240 chr1 46085194 46085368chr1 74575077 74575237 chr1 75036850 75039127 chr1 92185494 92185654chr1 99771278 99772551 chr1 115256420 115256599 chr1 115258670 115258781chr1 150477108 150477208 chr1 150550793 150550893 chr1 150727501150727601 chr1 151108103 151108203 chr1 151316207 151316307 chr1153177282 153177382 chr1 153430314 153430414 chr1 153907288 153907388chr1 154246293 154246393 chr1 154401746 154401846 chr1 155264358155264458 chr1 158627269 158627431 chr1 158632505 158632685 chr1162743258 162743386 chr1 162745441 162745633 chr1 162745925 162746160chr1 162748369 162748519 chr1 162749901 162750036 chr1 167095142167097827 chr1 175086109 175086340 chr1 175372326 175372736 chr1176563667 176564723 chr1 176709118 176709331 chr1 176915086 176915253chr1 177001609 177001965 chr1 190028421 190029374 chr1 190067151190068180 chr1 190203501 190203607 chr1 195246938 195247988 chr1195899530 195899738 chr1 196227361 196227562 chr1 237729889 237730050chr1 237777345 237778139 chr1 237886427 237886562 chr1 237947028237948233 chr1 247587188 247588870 chr1 248039226 248039722 chr9 85286358528735 chr9 9659339 9659439 chr9 10332505 10332605 chr9 1100570311005803 chr9 11677898 11677998 chr9 12352199 12352299 chr9 2190138321901483 chr9 21925971 21926071 chr9 21954943 21955043 chr9 2196818421968284 chr9 21968697 21968797 chr9 21970900 21971207 chr9 2197447521974826 chr9 21994137 21994330 chr9 24503905 24504079 chr9 119976663119977014 chr9 120474685 120476922 chr9 133738149 133738422 chr9133747508 133747608 chr9 133748246 133748424 chr9 133750254 133750439chr9 133753801 133753954 chr9 133755449 133755549 chr9 139390522139392010 chr9 139396723 139396940 chr9 139397633 139397782 chr9139399124 139399556 chr4 1803561 1803752 chr4 1805418 1805563 chr41806056 1806247 chr4 1806550 1806696 chr4 1807081 1807203 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4320462743204777 chr18 43249282 43249460 chr18 48604649 48604802 chr18 5068373250683864 chr18 54398650 54398796 chr18 60985524 60985667 chr18 6132828961328432 chr18 63477009 63477155 chr18 67563037 67563171 chr18 7052610470526248 chr18 74620319 74620472 chr19 1206890 1207229 chr19 12183851218525 chr19 1219295 1219431 chr19 1220350 1220519 chr19 12205501220724 chr19 1221180 1221361 chr19 1221905 1222046 chr19 12229601223204 chr19 1226420 1226679 chr19 4099176 4099272 chr19 40993764099456 chr19 4110481 4110621 chr19 4117451 4117524 chr19 41175914117663 chr19 10597293 10597511 chr19 10599833 10600090 chr19 1060030310600514 chr19 10602263 10602970 chr19 10610073 10610710 chr19 3093444630936220 chr19 30936236 30936655 chr19 31025721 31025947 chr19 3103885631040406 chr19 31767466 31769840 chr19 31769851 31770211 chr19 3177024631770670 chr19 46627115 46627261 chr19 56538529 56539902 chr19 5732514957325572 chr19 57325594 57325696 chr19 57325734 57328948 chr20 19609901961521 chr20 9546528 9546985 chr20 57766211 57769826 chr21 1104427611044355 chr21 11180816 11180887 chr21 11181036 11181190 chr21 1118124611181330 chr21 11181441 11181525 chr21 11181681 11181756 chr21 1118176611182005 chr21 21044257 21044478 chr21 44524394 44524462 chr21 4452446444524541 chr22 33559434 33559565 chr22 47892639 47892789 chr22 4821213448212283 chr22 48531984 48532124 chr22 48851889 48852027 chr22 4916801449168162 chr22 49819974 49820191 chrX 32429836 32430057 chrX 5449772554497925 chrX 111195250 111195640 chrX 112024145 112024364 chrX125298579 125298835 chrX 125298859 125299288 chrX 125299329 125299403chrX 125299449 125299801 chrX 125685269 125685520 chrX 125685544125685751 chrX 125685759 125685831 chrX 125685854 125685972 chrX125686009 125686084 chrX 125686129 125686562 chrX 135426542 135432592chrX 144903967 144906492 chrY 2712193 2712272 chrY 2722643 2722721 chrY2733178 2733258 chrY 2843138 2843272 chrY 2844743 2844868

TABLE 14 Chromosome Start (bp) End (bp) Gene chr13 32929222 32929396BRCA2 chr17 7576999 7577173 TP53 chr17 7578384 7578558 TP53 chr177579311 7579561 TP53 chr17 7577466 7577640 TP53 chr17 7578145 7578319TP53 chr17 41234419 41234593 BRCA1 chr17 7576802 7576976 TP53 chr1225398175 25398349 KRAS chr17 41275986 41276160 BRCA1 chr17 75738927574066 TP53 chr2 233990481 233990655 INPP5D chrX 153130773 153130955L1CAM chr13 32910624 32915006 BRCA2 chr14 96730550 96730769 BDKRB1 chr455604571 55604745 KIT chr12 57910672 57910846 DDIT3 chr12 5298145252981626 KRT72 chr6 43306871 43307045 ZNF318 chr6 136597137 136597311BCLAF1 chr12 38714202 38714376 ALG10B chr19 17435740 17435914 ANO8 chr1332972466 32972640 BRCA2 chrX 70823742 70823916 ACRC chr7 107824864107825038 NRCAM chr6 26156789 26157000 HIST1H1E chr7 100275114 100275288GNB2 chr11 72945430 72945604 P2RY2 chrX 149938722 149938896 CD99L2 chr1922271072 22271246 ZNF257 chr18 28714531 28714705 DSC1 chr6 169008790169008964 SMOC2 chr20 2778780 2778954 CPXM1 chr18 32398123 32398297 DTNAchr22 19241534 19241708 CLTCL1 chr17 74878219 74878393 MGAT5B chr8104709387 104709561 RIMS2 chr16 15844000 15844174 MYH11 chr17 4122300741223181 BRCA1 chr13 32906479 32907422 BRCA2 chr17 41243479 41246667BRCA1 chr17 41209023 41209197 BRCA1 chr14 65260180 65260354 SPTB chr1523811019 23811193 MKRN3 chr10 37430801 37430975 ANKRD30A chr8 139144840139145014 FAM135B chr6 170870945 170871119 TBP chr1 148753247 148753421NBPF16 chr17 38569103 38569277 TOP2A chr7 146829298 146829472 CNTNAP2chr14 102028203 102028378 DIO3 chr8 122640982 122641166 HAS2 chr346307409 46307597 CCR3 chr6 26251882 26252105 HIST1H2BH chr2 240982125240982350 PRR21 chr4 81967024 81967254 BMP3 chr1 247420018 247420261VN1R5 chrX 12938669 12939057 TLR8 chr19 31038886 31039138 ZNF536 chr1240076580 40076833 C12orf40 chrX 123517632 123518244 ODZ1 chr17 4125612141256295 BRCA1 chr9 94486985 94487159 ROR2 chr9 136507373 136507547 DBHchr14 88654294 88654468 KCNK10 chr6 167271630 167271804 RPS6KA2 chr853554964 53555138 RB1CC1 chr11 64428266 64428440 NRXN2 chr6 2779898627799160 HIST1H4K chr8 2000266 2000440 MYOM2 chr10 26385271 26385445MYO3A chr11 47869743 47869917 NUP160 chr2 29287839 29288013 C2orf71 chr774005154 74005328 GTF2IRD1 chr19 40398326 40398500 FCGBP chr13 3821136338211537 TRPC4 chr20 36030827 36031001 SRC chr4 189068354 189068528TRIML1 chr1 147126287 147126461 ACP6 chr17 3427487 3427661 TRPV3 chr2144836620 44836794 SIK1 chr2 170493291 170493465 PPIG chr6 133004283133004457 VNN1 chr13 48986112 48986286 LPAR6 chr22 40825603 40825777MKL1 chr10 76781752 76781926 KAT6B chr4 4199402 4199576 OTOP1 chr655119985 55120159 HCRTR2 chrX 29938051 29938225 IL1RAPL1 chr12 2089003620890210 SLCO1C1 chr13 32953452 32953626 BRCA2 chr22 29083842 29084016CHEK2 chr17 10350307 10350481 MYH4 chr1 176837967 176838141 ASTN1 chr1337015224 37015398 CCNA1 chr8 27293729 27293903 PTK2B chr12 114793625114793799 TBX5 chr3 9512454 9512628 SETD5 chr5 139743618 139743792SLC4A9 chr1 231401748 231401922 GNPAT chr9 37442028 37442202 ZBTB5 chr1156815447 156815621 INSRR chr12 132502750 132502924 EP400 chr5 53182335318407 ADAMTS16 chr9 133936421 133936595 LAMC3 chr22 17684479 17684653CECR1 chr9 111624625 111624799 ACTL7A chr6 130475999 130476173 SAMD3chr10 60549035 60549209 BICC1 chr1 203821227 203821401 ZC3H11A chr513770786 13770960 DNAH5 chr3 13896085 13896260 WNT7A chrX 3496181934962307 FAM47B chr1 159558178 159558367 APCS chr17 40997342 40997691AOC2 chr18 43534627 43534825 EPG5 chr5 24487959 24488158 CDH10 chr469816889 69817093 UGT2A3 chr19 52448710 52448914 ZNF613 chr5 96294059629772 TAS2R1 chr2 210517906 210518116 MAP2 chr9 990519 990733 DMRT3chrX 53577910 53578128 HUWE1 chr15 91424680 91424899 FURIN chr1477272844 77273066 ANGEL1 chr11 134252641 134252868 B3GAT1 chr19 5764087357641100 USP29 chr1 206224927 206225336 AVPR1B chr15 83932594 83932825BNC1 chr6 146350691 146351339 GRM1 chr2 202900305 202900539 FZD7 chr626273207 26273442 HIST1H2BI chr7 127222557 127222793 GCC1 chr5 3132302631323264 CDH6 chrX 127185082 127185520 ACTRT1 chr1 114483214 114483663HIPK1 chr17 29654577 29654837 NF1 chr22 40283477 40283743 ENTHD1 chr6169648554 169648826 THBS2 chr12 129558548 129559050 TMEM132D chr3134670329 134670616 EPHB1 chr2 223917650 223917940 KCNE4 chr6 127797196127797486 C6orf174 chr17 15234414 15234711 TEKT3 chr22 40814593 40814891MKL1 chr7 150417156 150417457 GIMAP1 chr19 40433543 40433852 FCGBP chr5140718608 140718920 PCDHGA2 chr8 21766910 21767224 DOK2 chrX 129518508129518825 GPR119 chrX 12736567 12736891 FRMPD4 chr10 98714824 98715150LCOR chr6 26056314 26056647 HIST1H1C chr11 128680372 128680706 FLI1chr10 26463052 26463392 MYO3A chr1 18691777 18692119 IGSF21 chr1248039432 248039775 TRIM58 chrX 30873210 30873558 TAB3 chr6 2615840326158752 HIST1H2BD chr7 27135133 27135486 HOXA1 chr21 31538435 31538793CLDN17 chr2 46985990 46986360 SOCS5 chr19 58967128 58967499 ZNF324Bchr21 38309122 38309498 HLCS chr3 142840773 142841153 CHST2 chr388040207 88040589 HTR1F chr1 237777437 237777821 RYR2 chrX 2341167523412063 PTCHD1 chr1 117122025 117122414 IGSF3 chr16 55532193 55532367MMP2 chr18 30260367 30260541 KLHL14 chr2 157186233 157186407 NR4A2 chr11121391435 121391609 SORL1 chr21 14982944 14983118 POTED chr17 4122630341226477 BRCA1 chr4 113570679 113570853 LARP7 chr22 39112731 39112905GTPBP1 chr1 180023506 180023680 CEP350 chrX 62898319 62898493 ARHGEF9chr8 89128777 89128951 MMP16 chr21 15872896 15873070 SAMSN1 chr132381454 32381628 PTP4A2 chr3 138383874 138384048 PIK3CB chr1 4619329946193473 IPP chr4 68619760 68619934 GNRHR chr4 74853659 74853833 PPBPchrX 44937610 44937784 KDM6A chr12 109536165 109536339 UNG chr1151755349 151755523 TDRKH chr19 54744228 54744402 LILRA6 chr6 3193764031937814 DOM3Z chr3 50005006 50005180 RBM6 chrX 100617525 100617699 BTKchr7 48017994 48018168 HUS1 chr19 19371633 19371807 HAPLN4 chr7 9900098799001161 PDAP1 chr17 7358601 7358775 CHRNB1 chr7 29980244 29980418 SCRN1chr2 62067255 62067429 FAM161A chr13 95121123 95121297 DCT chr10134008343 134008517 DPYSL4 chr4 524344 524518 PIGG chr20 3075308530753259 TM9SF4 chr1 72076681 72076855 NEGR1 chr19 52520314 52520488ZNF614 chr6 137322921 137323095 IL20RA chr9 103212884 103213058 C9orf30chr14 76905712 76905886 ESRRB chr15 41687069 41687243 NDUFAF1 chr2250869686 50869860 PPP6R2 chr2 207804270 207804444 CPO chr8 3769050737690681 GPR124 chr6 5771505 5771679 FARS2 chr7 31124313 31124487ADCYAP1R1 chr1 207785038 207785212 CR1 chr14 51087293 51087467 ATL1 chr8124195401 124195575 FAM83A chr11 30255107 30255281 FSHB chr12 27885782788752 CACNA1C chr1 179013079 179013253 FAM20B chrX 5821584 5821758NLGN4X chr2 114500190 114500364 SLC35F5 chr12 101490282 101490456 ANO4chr5 148392134 148392308 SH3TC2 chr12 10962009 10962183 TAS2R9 chr232640289 32640463 BIRC6 chr18 70417719 70417893 NETO1 chr18 7045097970451153 NETO1 chr9 95400367 95400541 IPPK chr13 35615170 35615344 NBEAchr7 55259402 55259576 EGFR chr7 55273137 55273311 EGFR chr4 186066207186066381 SLC25A4 chr19 47197125 47197299 PRKD2 chr6 127608323 127608497RNF146 chr17 37868153 37868327 ERBB2 chr17 37881530 37881704 ERBB2 chr1774289654 74289828 QRICH2 chr9 4117788 4117962 GLIS3 chr2 131785500131785674 ARHGEF4 chrX 153760785 153760959 G6PD chr13 113803617113803791 F10 chr18 33848484 33848658 MOCOS chr19 55106319 55106493LILRA1 chr6 152658007 152658181 SYNE1 chr3 5024988 5025162 BHLHE40 chr653518901 53519075 KLHL31 chr1 11078772 11078946 TARDBP chr5 5458109554581269 DHX29 chr21 45987677 45987851 TSPEAR chrX 107403742 107403916COL4A6 chr2 125530315 125530489 CNTNAP5 chr10 135076611 135076785 ADAM8chr12 85517858 85518032 LRRIQ1 chr10 105330702 105330876 NEURL chr935107570 35107744 FAM214B chr7 16505155 16505329 SOSTDC1 chrX 3116542731165601 DMD chrX 32583852 32584026 DMD chr5 7626267 7626441 ADCY2 chr57695833 7696007 ADCY2 chr5 7802316 7802490 ADCY2 chr3 8609117 8609291LMCD1 chr10 117026251 117026425 ATRNL1 chr1 55172071 55172245 HEATR8chr20 35862373 35862547 RPN2 chr17 56557443 56557617 HSF5 chr10120920366 120920540 SFXN4 chr2 65559065 65559239 SPRED2 chr11 108277762108277936 C11orf65 chr1 89730488 89730662 GBP5 chr16 46781757 46781931MYLK3 chr20 44507103 44507277 ZSWIM3 chr6 27861270 27861444 HIST1H2BOchr12 103984654 103984828 STAB2 chr22 46327046 46327220 WNT7B chr136645452 36645626 MAP7D1 chr13 36049712 36049886 MAB21L1 chr1 149857811149857985 HIST2H2BE chr17 18003840 18004014 DRG2 chr12 53663669 53663843ESPL1 chr12 53676046 53676220 ESPL1 chr3 48040190 48040364 MAP4 chr612122606 12122780 HIVEP1 chr19 54849375 54849549 LILRA4 chr11 9473165894731832 KDM4D chr2 109964146 109964320 SH3RF3 chr2 96040018 96040192KCNIP3 chr7 23296510 23296684 GPNMB chr14 24845534 24845708 NFATC4 chr2222324675 22324849 TOP3B chr4 71114706 71114880 CSN3 chr11 117302257117302431 DSCAML1 chr17 37676187 37676361 CDK12 chr4 88766974 88767148MEPE chr1 181745213 181745387 CACNA1E chr9 463514 463688 DOCK8 chr2040081366 40081540 CHD6 chr20 40111927 40112101 CHD6 chr1 186113302186113476 HMCN1 chr15 64791917 64792091 ZNF609 chr3 184001547 184001721ECE2 chrX 53423377 53423551 SMC1A chrX 53432438 53432612 SMC1A chr5135692360 135692534 TRPC7 chr1 225706994 225707168 ENAH chr1 216850514216850688 ESRRG chr2 68882394 68882568 PROKR1 chr7 87144562 87144736ABCB1 chr10 75276690 75276864 USP54 chr8 95172209 95172383 CDH17 chr872233954 72234128 EYA1 chr2 200137240 200137414 SATB2 chrX 134706738134706912 DDX26B chr17 10535809 10535983 MYH3 chr15 75188492 75188666MPI chr12 5708635 5708809 ANO2 chr18 644905 645079 CLUL1 chr2 8562890685629080 CAPG chr3 78987947 78988121 ROBO1 chr7 2257550 2257724 MAD1L1chr1 11561350 11561524 PTCHD2 chr12 104171567 104171741 NT5DC3 chr1421024722 21024896 RNASE9 chr7 107342250 107342424 SLC26A4 chr14 7220571972205893 SIPA1L1 chr5 3599655 3599829 IRX1 chr1 24077339 24077513 TCEB3chr11 47333250 47333424 MADD chr4 46305465 46305639 GABRA2 chr9136405705 136405879 ADAMTSL2 chr6 30572351 30572525 PPP1R10 chr540976809 40976983 C7 chr6 117010462 117010636 KPNA5 chr1 145440001145440175 TXNIP chr1 236918334 236918508 ACTN2 chr20 30915324 30915498KIF3B chr4 175598247 175598421 GLRA3 chr4 70512921 70513095 UGT2A1 chr177636373 7636547 DNAH2 chr2 183960180 183960354 DUSP19 chrX 105011258105011432 IL1RAPL2 chr2 220115767 220115941 TUBA4A chr8 144942360144942534 EPPK1 chr3 89259371 89259545 EPHA3 chr3 89456382 89456556EPHA3 chr20 34241968 34242142 RBM12 chr6 33245175 33245349 B3GALT4 chr1759560332 59560506 TBX4 chr12 56355086 56355260 PMEL chr10 5122495451225128 AGAP8 chr11 56949740 56949914 LRRC55 chrX 47497448 47497622ELK1 chrX 92927506 92927680 NAP1L3 chr10 26315296 26315470 MYO3A chr1936002300 36002474 DMKN chr19 12986845 12987019 DNASE2 chr6 3172784231728016 MSH5 chr17 42745332 42745506 C17orf104 chrX 18234688 18234862BEND2 chr21 41414479 41414653 DSCAM chr21 41457523 41457697 DSCAM chr1251092093 51092267 DIP2B chr6 161027513 161027687 LPA chr17 6355432963554503 AXIN2 chr4 155505472 155505646 FGA chr4 155506836 155507010 FGAchr12 7456950 7457124 ACSM4 chr19 58016002 58016176 ZNF773 chr6150001356 150001530 LATS1 chr3 96706157 96706331 EPHA6 chr7 107204269107204443 COG5 chr14 65262131 65262305 SPTB chr1 70502178 70502352 LRRC7chr6 145956389 145956563 EPM2A chr3 5249775 5249949 EDEM1 chr8 143961043143961217 CYP11B1 chr20 44678257 44678431 SLC12A5 chr6 27222972 27223146PRSS16 chr9 125014101 125014275 RBM18 chr3 193042640 193042814 ATP13A5chr3 193052715 193052889 ATP13A5 chr11 76900364 76900538 MYO7A chr330729852 30730026 TGFBR2 chr5 112769819 112769993 TSSK1B chr8 145153751145153925 SHARPIN chr12 29648216 29648390 OVCH1 chr6 33236263 33236437VPS52 chr22 22277480 22277654 PPM1F chr7 101844836 101845010 CUX1 chr7101882677 101882851 CUX1 chr10 61967835 61968009 ANK3 chr17 3432840634328580 CCL15 chr7 73944013 73944187 GTF2IRD1 chr5 167928970 167929144RARS chr2 170393706 170393880 FASTKD1 chr3 136708257 136708431 IL20RBchr3 51399271 51399445 DOCK3 chr3 56667149 56667323 FAM208A chr1951649056 51649230 SIGLEC7 chr6 47649589 47649763 GPR111 chr20 6041971460419888 CDH4 chr1 32671739 32671913 IQCC chr1 32673139 32673313 IQCCchr4 87730927 87731101 PTPN13 chr20 1960986 1961160 PDYN chr4 84656778465851 METTL19 chr2 167094603 167094777 SCN9A chr3 42739739 42739913HHATL chr14 92343897 92344071 FBLN5 chr5 36035812 36035986 UGT3A2 chr633165518 33165692 RXRB chr3 183860533 183860707 EIF2B5 chr11 121000695121000869 TECTA chr6 26205002 26205176 HIST1H4E chr22 24583132 24583306SUSD2 chr13 32731388 32731562 FRY chr15 28474329 28474503 HERC2 chr146080972 46081146 NASP chr13 92408524 92408698 GPC5 chr16 5778702957787203 KATNB1 chr8 145763063 145763237 ARHGAP39 chr5 179228536179228710 MGAT4B chr19 54867898 54868072 LAIR1 chr4 146058683 146058857OTUD4 chr11 7984761 7984935 NLRP10 chr19 7550769 7550943 PEX11G chr2035127947 35128121 DLGAP4 chr9 34564547 34564721 CNTFR chr19 4036839340368567 FCGBP chr14 77237473 77237647 VASH1 chrX 128724097 128724271OCRL chr4 70346323 70346497 UGT2B4 chr13 52604229 52604403 UTP14C chr827327346 27327520 CHRNA2 chr6 53989304 53989478 MLIP chr6 5409554354095717 MLIP chr2 166797528 166797702 TTC21B chr17 78168973 78169147CARD14 chr10 61574374 61574548 CCDC6 chr20 46277713 46277887 NCOA3 chr6108214685 108214859 SEC63 chr8 145689615 145689789 CYHR1 chr17 4759005147590225 NGFR chr7 37907379 37907553 TXNDC3 chr6 87725173 87725347 HTR1Echr3 123695682 123695856 ROPN1 chr7 29546825 29546999 CHN2 chrX119077195 119077369 NKAP chr1 201182610 201182784 IGFN1 chrX 2372384223724016 ACOT9 chr8 98289303 98289477 TSPYL5 chr2 26696267 26696441 OTOFchr6 97051481 97051655 FHL5 chr20 17434466 17434640 PCSK2 chr1 192128313192128487 RGS18 chr15 43438674 43438848 TMEM62 chr20 43942089 43942263RBPJL chrX 24226323 24226497 ZFX chr1 152195573 152195747 HRNR chrX15305982 15306156 ASB11 chr19 15079126 15079300 SLC1A6 chr9 8893776688937940 ZCCHC6 chr19 12384451 12384625 ZNF44 chr7 75959322 75959496YWHAG chr6 33154432 33154606 COL11A2 chr10 75557566 75557740 KIAA0913chr14 60903582 60903756 C14orf39 chr22 22160094 22160268 MAPK1 chr1211338742 11338916 TAS2R42 chr15 90145016 90145190 C15orf42 chr1221693338 21693512 GYS2 chr2 197737131 197737305 PGAP1 chr17 82154608215634 ARHGEF15 chr6 49427010 49427184 MUT chr3 52525895 52526069 NISCHchr12 49087834 49088008 CCNT1 chr3 195295787 195295961 APOD chr1952001316 52001490 SIGLEC12 chr10 18940007 18940181 NSUN6 chr7 134135508134135682 AKR1B1 chrX 135579769 135579943 HTATSF1 chr4 5843007 5843181CRMP1 chrX 21674122 21674296 KLHL34 chrX 13727247 13727421 RAB9A chr5147820656 147820830 FBXO38 chr16 16208588 16208762 ABCC1 chr17 1796214517962319 C17orf39 chr20 43384832 43385006 RIMS4 chr2 200820452 200820626C2orf47 chr10 104679436 104679610 CNNM2 chr14 64954556 64954730 ZBTB25chr4 80246377 80246551 NAA11 chr6 90642071 90642245 BACH2 chr17 7947831179478485 ACTG1 chr3 111672739 111672913 PHLDB2 chr19 50939845 50940019MYBPC2 chr9 91616992 91617166 S1PR3 chr2 165550757 165550931 COBLL1chr17 45299047 45299221 MYL4 chr1 46489385 46489559 MAST2 chr1 4650161146501785 MAST2 chr15 65499237 65499411 CILP chr4 57220203 57220377 AASDHchr2 10186272 10186446 KLF11 chr5 169483642 169483816 DOCK2 chr1585383879 85384053 ALPK3 chr1 27720852 27721026 GPR3 chr1 173961961173962135 RC3H1 chr7 126746533 126746707 GRM8 chr8 119391834 119392008SAMD12 chr7 12691408 12691582 SCIN chr12 8083882 8084056 SLC2A3 chr1257032917 57033091 ATP5B chr8 139180127 139180301 FAM135B chr1 211486062211486236 RCOR3 chr2 206641089 206641263 NRP2 chr1 209964082 209964256IRF6 chr10 75107866 75108040 TTC18 chr1 150483510 150483684 ECM1 chr1128134957 28135131 METTL15 chr1 45243348 45243522 RPS8 chr16 2891311128913285 ATP2A1 chr7 154760639 154760813 PAXIP1 chr3 113955346 113955520ZNF80 chr10 98133355 98133529 TLL2 chr8 8998346 8998520 PPP1R3B chr1916314260 16314434 AP1M1 chr9 75435793 75435967 TMC1 chr19 1979090619791080 ZNF101 chr6 40399994 40400168 LRFN2 chr1 176668469 176668643PAPPA2 chr19 34900072 34900246 PDCD2L chr15 66850053 66850227 LCTL chr2040727039 40727213 PTPRT chr8 2819987 2820161 CSMD1 chr8 2875998 2876172CSMD1 chr8 3266941 3267115 CSMD1 chr2 43969878 43970052 PLEKHH2 chr14105212560 105212734 ADSSL1 chr9 98209437 98209611 PTCH1 chr9 9823981998239993 PTCH1 chr2 165349531 165349705 GRB14 chr11 77937720 77937894GAB2 chr1 12409267 12409441 VPS13D chr6 31931738 31931912 SKIV2L chr12123276530 123276704 CCDC62 chr11 76174928 76175102 C11orf30 chr136367522 36367696 EIF2C1 chr7 149517954 149518128 SSPO chr6 2847203728472211 GPX6 chr9 128083689 128083863 GAPVD1 chr2 108478035 108478209RGPD4 chr13 75868982 75869156 TBC1D4 chr1 110950222 110950396 HBXIPchr19 8491478 8491652 MARCH2 chr7 99711228 99711402 TAF6 chr5 3938303339383207 DAB2 chr11 75282953 75283127 SERPINH1 chr12 53012017 53012191KRT73 chr11 67225828 67226002 CABP4 chr15 101595261 101595435 LRRK1 chr2175618241 175618415 CHRNA1 chr10 111624918 111625092 XPNPEP1 chr626107915 26108089 HIST1H1T chr2 96781270 96781444 ADRA2B chr19 5526380755263981 KIR2DL3 chr18 24496257 24496431 CHST9 chr15 42041402 42041576MGA chr7 104783598 104783772 SRPK2 chr19 48922466 48922640 GRIN2D chr454256654 54256828 FIP1L1 chr16 24358009 24358183 CACNG3 chr19 5271592552716099 PPP2R1A chr8 133763964 133764138 TMEM71 chr17 73490958 73491132KIAA0195 chr3 119219537 119219711 TIMMDC1 chrX 54472681 54472855 FGD1chr20 52644908 52645082 BCAS1 chr6 30309744 30309918 TRIM39 chr1237659923 237660097 RYR2 chr1 237863502 237863676 RYR2 chr7 9855376098553934 TRRAP chr7 50611556 50611730 DDC chr11 92495033 92495207 FAT3chr6 56497682 56497856 DST chr4 46994816 46994990 GABRA4 chr14 5785815857858332 NAA30 chr2 178936438 178936612 PDE11A chr11 60889086 60889260CD5 chr9 4663050 4663224 PPAPDC2 chr20 58448885 58449059 SYCP2 chr1581585182 81585356 IL16 chr1 32202161 32202335 BAI2 chr1 3222186232222036 BAI2 chr1 12939612 12939786 PRAMEF4 chr2 225266098 225266272FAM124B chr17 10317200 10317374 MYH8 chr2 178082415 178082589 HNRNPA3chr6 132171100 132171274 ENPP1 chr6 132211478 132211652 ENPP1 chr1048370965 48371139 ZNF488 chr12 52093330 52093504 SCN8A chr12 5211544152115615 SCN8A chr11 116730074 116730248 SIK3 chr6 31541057 31541231 LTAchr1 12837214 12837388 PRAMEF12 chr15 41099812 41099986 ZFYVE19 chr1733312991 33313165 LIG3 chr16 58711191 58711365 SLC38A7 chr3 137717742137717916 CLDN18 chr5 160047616 160047790 ATP10B chr3 130290015130290189 COL6A6 chrX 142596647 142596821 SPANXN3 chr2 88387334 88387508SMYD1 chr12 4479674 4479848 FGF23 chr1 153004877 153005051 SPRR1B chrX48678500 48678674 HDAC6 chr12 7842774 7842948 GDF3 chr7 121943781121943955 FEZF1 chr1 156264564 156264738 C1orf85 chr16 57957143 57957317CNGB1 chr5 16478940 16479114 FAM134B chrX 107930747 107930921 COL4A5chr9 74840559 74840733 GDA chr7 116339781 116339955 MET chr4 42853214285495 LYAR chr12 6838414 6838588 COPS7A chr5 72469046 72469220 TMEM174chr12 116406736 116406910 MED13L chr19 39321984 39322158 ECH1 chr1543571307 43571481 TGM7 chr17 76522932 76523106 DNAH17 chr5 454021 454195EXOC3 chr1 53540222 53540396 PODN chr2 198363398 198363572 HSPD1 chr1070502159 70502333 CCAR1 chr1 70715584 70715758 SRSF11 chr2 234652205234652379 DNAJB3 chr15 52571701 52571875 MYO5C chrX 153540967 153541141TKTL1 chr16 74499555 74499729 GLG1 chr1 85397107 85397281 MCOLN2 chr63015733 3015907 NQO2 chr6 73787041 73787215 KCNQ5 chrX 40539998 40540172MED14 chr11 93754532 93754706 HEPHL1 chr9 112899635 112899809PALM2-AKAP2 chr20 30414583 30414757 MYLK2 chr11 58604481 58604655GLYATL2 chr10 105362405 105362579 SH3PXD2A chr4 154702644 154702818SFRP2 chr4 72994361 72994535 NPFFR2 chr17 48595964 48596138 MYCBPAPchr16 84035388 84035562 NECAB2 chr9 23692564 23692738 ELAVL2 chr8113562968 113563142 CSMD3 chr9 12694106 12694280 TYRP1 chr15 102226094102226268 TARSL2 chr2 86255002 86255176 POLR1A chr9 112899635 112899809AKAP2 chr8 57228704 57228878 SDR16C5 chrX 123040810 123040984 XIAP chr1914862190 14862364 EMR2 chr1 215963468 215963642 USH2A chr1 216595236216595410 USH2A chr17 29485982 29486156 NF1 chr17 29550436 29550610 NF1chr20 46365379 46365553 SULF2 chr9 108424841 108425015 TAL2 chr3142511650 142511824 TRPC1 chr19 15794318 15794492 CYP4F12 chr12 7289325972893433 TRHDE chr16 65005813 65005987 CDH11 chr16 22278014 22278188EEF2K chrX 100276916 100277090 TRMT2B chr12 44913848 44914022 NELL2chr19 6750496 6750670 TRIP10 chr10 98824510 98824684 SLIT1 chr6 7451780174517975 CD109 chr7 45697317 45697491 ADCY1 chr12 20885899 20886073SLCO1C1 chr2 220315842 220316016 SPEG chr4 13617017 13617191 BOD1L chr1168703650 68703824 IGHMBP2 chr14 70245109 70245283 SLC10A1 chr13 3295078032950954 BRCA2 chr11 102587004 102587178 MMP8 chr15 72170402 72170576MYO9A chr2 187558916 187559090 FAM171B chr2 187615858 187616032 FAM171Bchr19 45992597 45992771 RTN2 chr7 18067160 18067334 PRPS1L1 chr4124323250 124323424 SPRY1 chr20 3127359 3127533 FASTKD5 chrX 1938082419380998 MAP3K15 chr19 35719285 35719459 FAM187B chr2 1906858 1907032MYT1L chr12 41407977 41408151 CNTN1 chr5 1335143 1335317 CLPTM1L chr2131904190 131904364 PLEKHB2 chr20 5294571 5294745 PROKR2 chr1 211749217211749391 SLC30A1 chr4 52938151 52938325 SPATA18 chr12 108011953108012127 BTBD11 chr22 29091692 29091866 CHEK2 chr1 37346324 37346498GRIK3 chr1 37356489 37356663 GRIK3 chr7 54612310 54612484 VSTM2A chr11684333 1684507 NADK chrX 69646940 69647114 GDPD2 chr3 151105648151105822 MED12L chr11 64627490 64627664 EHD1 chr16 22926315 22926489HS3ST2 chrX 130220498 130220672 ARHGAP36 chr8 133144381 133144555 KCNQ3chr14 26917857 26918031 NOVA1 chr10 79576296 79576470 DLG5 chr1079595459 79595633 DLG5 chr2 27375540 27375714 TCF23 chr16 1072137610721550 TEKT5 chr16 67974118 67974292 LCAT chr1 154987548 154987722ZBTB7B chr10 21414826 21415000 C10orf113 chr13 42407499 42407673KIAA0564 chr11 68552295 68552469 CPT1A chr1 110168907 110169081 AMPD2chrX 51639534 51639708 MAGED1 chr5 40716351 40716525 TTC33 chr1721207714 21207888 MAP2K3 chr17 29622546 29622720 OMG chr1 1944202519442199 UBR4 chr1 19443804 19443978 UBR4 chr1 15793869 15794043 CELA2Achr2 120194564 120194738 TMEM37 chr16 30507349 30507523 ITGAL chr2061907410 61907584 ARFGAP1 chr2 226273609 226273783 NYAP2 chr20 5503340855033582 CASS4 chr1 245704099 245704273 KIF26B chr16 67576751 67576925FAM65A chr11 62365743 62365917 MTA2 chr16 343549 343723 AXIN1 chr1950548092 50548266 ZNF473 chr9 132631949 132632123 USP20 chr1 3323613933236313 KIAA1522 chr10 390907 391081 DIP2C chr6 137019657 137019831MAP3K5 chr14 78161074 78161248 ALKBH1 chr3 35778726 35778900 ARPP21 chr345942952 45943126 CCR9 chr13 42772585 42772759 DGKH chr2 2760086027601034 ZNF513 chr11 89424102 89424276 FOLH1B chr12 60164984 60165158SLC16A7 chr2 179718181 179718355 CCDC141 chr1 156214551 156214725 PAQR6chr6 32006162 32006336 CYP21A2 chr7 107423636 107423810 SLC26A3 chr1955748033 55748207 PPP6R1 chr15 92663689 92663863 SLCO3A1 chr22 4204952642049700 XRCC6 chr20 45891014 45891188 ZMYND8 chr8 143994696 143994870CYP11B2 chr4 155491574 155491748 FGB chr13 79175686 79175860 POU4F1chr11 118772305 118772479 BCL9L chr6 33272099 33272273 TAPBP chr1953912197 53912371 ZNF765 chr13 109318331 109318505 MYO16 chr19 5642380356423977 NLRP13 chr7 129929427 129929601 CPA2 chr1 66036244 66036418LEPR chr1 145281532 145281706 NOTCH2NL chr6 100838676 100838850 SIM1chr8 77618010 77618184 ZFHX4 chr5 19483416 19483590 CDH18 chr6 117130543117130717 GPRC6A chr14 94120241 94120415 UNC79 chr4 114253089 114253263ANK2 chr2 152293722 152293896 RIF1 chr19 36214546 36214720 MLL4 chr1936218419 36218593 MLL4 chr12 39733988 39734162 KIF21A chr12 2567285125673025 IFLTD1 chr12 25679005 25679179 IFLTD1 chr1 161163732 161163906ADAMTS4 chr12 13768037 13768211 GRIN2B chrX 153662568 153662742 ATP6AP1chr12 81205280 81205454 LIN7A chr19 49116355 49116529 FAM83E chr220205962 20206136 MATN3 chr2 159519407 159519581 PKP4 chr6 146256160146256334 SHPRH chr9 101900165 101900339 TGFBR1 chr8 130760723 130760897GSDMC chr2 158399208 158399382 ACVR1C chr1 43786872 43787046 TIE1 chr626271310 26271484 HIST1H3G chr6 54214591 54214765 TINAG chr13 8009494080095114 NDFIP2 chr1 222717091 222717265 HHIPL2 chr2 219498318 219498492PLCD4 chr1 43825356 43825530 CDC20 chr1 60505676 60505850 C1orf87 chr1257501916 57502090 STAT6 chr17 78063535 78063709 CCDC40 chr6 3051391630514090 GNL1 chr6 30521123 30521297 GNL1 chr21 34882071 34882245 GARTchr19 2799650 2799824 THOP1 chr19 43375863 43376037 PSG1 chr2 135711761135711935 CCNT2 chr22 38877292 38877466 KDELR3 chr5 161117195 161117369GABRA6 chr5 161118994 161119168 GABRA6 chr1 236751208 236751382 HEATR1chr4 165118140 165118314 ANP32C chr8 17400875 17401049 SLC7A2 chr1761560782 61560956 ACE chr7 128415710 128415884 OPN1SW chrX 153129767153129941 L1CAM chr1 173839479 173839653 ZBTB37 chr17 40854849 40855023EZH1 chr15 75042230 75042404 CYP1A2 chr2 27427641 27427815 SLC5A6 chr202636009 2636183 NOP56 chr3 19384055 19384229 KCNH8 chr14 2504353225043706 CTSG chr3 178935972 178936146 PIK3CA chr8 120118077 120118251COLEC10 chr12 56487159 56487333 ERBB3 chr12 56495315 56495489 ERBB3 chr220403715 20403889 SDC1 chr1 79093604 79093778 IFI44L chr17 4982499949825173 CA10 chr17 11672436 11672610 DNAH9 chr11 60687162 60687336TMEM109 chr8 41615517 41615691 ANK1 chr7 87068980 87069154 ABCB4 chr189887055 9887229 TXNDC2 chr20 43034707 43034881 HNF4A chrX 153418409153418583 OPN1LW chr6 82924148 82924322 IBTK chr20 54578958 54579132CBLN4 chr7 95442491 95442665 DYNC1I1 chr22 44011666 44011840 EFCAB6chr21 43327743 43327917 C2CD2 chr17 43213839 43214013 ACBD4 chr535876286 35876460 IL7R chr3 38520603 38520777 ACVR2B chr17 7236843872368612 GPR142 chr8 25261069 25261243 DOCK5 chr19 51519278 51519452KLK10 chr2 238249485 238249659 COL6A3 chr2 238274538 238274712 COL6A3chr8 10480565 10480739 RP1L1 chr18 13438225 13438399 C18orf1 chr12126004061 126004235 TMEM132B chr12 126135275 126135449 TMEM132B chr155472663 55472837 BSND chr4 90816143 90816317 MMRN1 chr9 137716397137716571 COL5A1 chr7 5540135 5540309 FBXL18 chr1 173873047 173873221SERPINC1 chr18 8819056 8819230 CCDC165 chr5 149435557 149435731 CSF1Rchr14 39650083 39650257 PNN chr8 1812483 1812657 ARHGEF10 chr9 139390924139391098 NOTCH1 chr16 66420661 66420835 CDH5 chr7 72420355 72420529NSUN5P2 chr5 41153950 41154124 C6 chr7 72727151 72727325 TRIM50 chr12101723050 101723224 UTP20 chr17 42330511 42330685 SLC4A1 chr11 122726396122726570 CRTAM chr3 113329835 113330009 SIDT1 chr18 77063572 77063746ATP9B chr3 70014056 70014230 MITE chr19 49485484 49485658 GYS1 chr3155560227 155560401 SLC33A1 chr12 51868091 51868265 SLC4A8 chr4 155338155512 ZNF718 chr6 144783714 144783888 UTRN chr10 92509125 92509299 HTR7chr12 132561969 132562143 EP400 chr6 26406085 26406259 BTN3A1 chr1151400732 151400906 POGZ chr22 32253397 32253571 DEPDC5 chr19 3898076338980937 RYR1 chr12 40692897 40693071 LRRK2 chr9 117139290 117139464AKNA chr6 51882228 51882402 PKHD1 chr11 85685715 85685889 PICALM chr12110878067 110878241 ARPC3 chr19 36333279 36333453 NPHS1 chr13 7833510478335278 SLAIN1 chr15 44951341 44951515 SPG11 chr15 44952639 44952813SPG11 chr16 84203438 84203612 DNAAF1 chr19 17132828 17133002 CPAMD8 chrX53279451 53279625 IQSEC2 chr6 29589474 29589648 GABBR1 chr11 132016119132016293 NTM chr10 5247671 5247845 AKR1C4 chr6 99796959 99797133C6orf168 chr8 143570660 143570834 BAI1 chr6 94120655 94120829 EPHA7 chr249381393 49381567 FSHR chr19 53344324 53344498 ZNF468 chr3 5385199653852170 CHDH chr10 55568373 55568547 PCDH15 chr10 55955408 55955582PCDH15 chr2 112566559 112566733 ANAPC1 chr3 66431018 66431192 LRIG1 chr2207636592 207636766 FASTKD2 chr3 161221163 161221337 OTOL1 chr6 7298400672984180 RIMS1 chrX 24329584 24329758 FAM48B2 chr2 24345259 24345433PFN4 chr16 12145820 12145994 SNX29 chr16 27788923 27789097 KIAA0556 chr897251691 97251865 MTERFD1 chr6 99857047 99857221 PNISR chr15 6396463763964811 HERC1 chr10 121586905 121587079 INPP5F chr1 156496247 156496421IQGAP3 chr17 45216093 45216267 CDC27 chr6 71011653 71011827 COL9A1 chr8119122814 119122988 EXT1 chr16 86601243 86601417 FOXC2 chr19 5689562356895797 ZNF582 chr5 37815979 37816153 GDNF chr14 20851339 20851513 TEP1chr16 67000610 67000784 CES3 chr4 1742545 1742719 TACC3 chr1 4406935844069532 PTPRF chr19 16000247 16000421 CYP4F2 chr1 34076610 34076784CSMD2 chr5 131606566 131606740 PDLIM4 chr12 7585995 7586169 CD163L1chr12 54893106 54893280 NCKAP1L chr8 22064343 22064517 BMP1 chr1348955419 48955593 RB1 chrX 108652224 108652398 GUCY2F chr1 3216344832163622 COL16A1 chr4 153273751 153273925 FBXW7 chr12 75816629 75816803GLIPR1L2 chr22 35481460 35481634 ISX chr21 10944640 10944814 TPTE chrX7268137 7268311 STS chr2 121708832 121709006 GLI2 chr4 160264407160264581 RAPGEF2 chr10 100017735 100017909 LOXL4 chr16 3098282430982998 SETD1A chr17 36070503 36070677 HNF1B chr17 36093557 36093731HNF1B chr1 200843017 200843191 GPR25 chr17 54912192 54912366 DGKE chr1620974568 20974742 DNAH3 chr16 20981170 20981344 DNAH3 chr2 217142438217142612 MARCH4 chr11 126306719 126306893 KIRREL3 chr14 7974665879746832 NRXN3 chr12 109660573 109660747 ACACB chr1 149761698 149761872FCGR1A chr2 163144642 163144816 IFIH1 chr1 156131135 156131309 SEMA4Achr12 93881267 93881441 MRPL42 chrX 135572470 135572644 BRS3 chr1162519888 62520062 ZBTB3 chr13 47466518 47466692 HTR2A chr11 6847826668478440 MTL5 chr6 47976543 47976717 C6orf138 chr16 4700337 4700511MGRN1 chr17 47246905 47247079 B4GALNT2 chr12 11139352 11139526 TAS2R50chr7 150068715 150068889 REPIN1 chr6 117710580 117710754 ROS1 chr1954377174 54377348 MYADM chrX 77369231 77369405 PGK1 chr2 1535894415359118 NBAS chr2 15468292 15468466 NBAS chr16 24902178 24902352SLC5A11 chr3 47098628 47098802 SETD2 chr14 64457120 64457294 SYNE2 chr669348908 69349082 BAI3 chr6 69703678 69703852 BAI3 chr9 123199555123199729 CDK5RAP2 chr14 47389201 47389375 MDGA2 chr1 17275270 17275444CROCC chr11 47290086 47290260 NR1H3 chr20 58330260 58330434 PHACTR3 chr157476794 57476968 DAB1 chr2 162881294 162881468 DPP4 chr1 1191833511918509 NPPB chr1 177901799 177901973 SEC16B chr9 101829150 101829324COL15A1 chr19 47151866 47152040 DACT3 chr6 30457597 30457771 HLA-E chr45624280 5624454 EVC2 chr19 55179309 55179483 LILRB4 chrX 130408531130408705 IGSF1 chr1 110019372 110019546 SYPL2 chr21 16337294 16337468NRIP1 chr2 160086537 160086711 TANC1 chr17 65026809 65026983 CACNG4 chr3160120511 160120685 SMC4 chr1 115256403 115256577 NRAS chr19 3636996036370134 APLP1 chr18 74563725 74563899 ZNF236 chr5 132270198 132270372AFF4 chr6 31557561 31557735 NCR3 chrX 154019995 154020169 MPP1 chr650696878 50697052 TFAP2D chr6 50740350 50740524 TFAP2D chr19 3623759136237765 PSENEN chr4 6374233 6374407 PPP2R2C chr12 6077225 6077399 VWFchr8 43152132 43152306 POTEA chr1 145681971 145682145 RNF115 chr1173545762 173545936 SLC9A11 chr1 47401169 47401343 CYP4A11 chrX142795485 142795659 SPANXN2 chr7 19184656 19184830 FERD3L chr1 234367158234367332 SLC35F3 chr16 70954630 70954804 HYDIN chr16 70977696 70977870HYDIN chr10 70101685 70101859 HNRNPH3 chr10 71562298 71562472 COL13A1chr10 30629135 30629309 MTPAP chr19 49685959 49686133 TRPM4 chr1226784510 226784684 C1orf95 chr18 28993305 28993479 DSG4 chrX 8595001585950189 DACH2 chr14 60591751 60591925 C14orf135 chr3 119133904119134078 ARHGAP31 chr13 48664433 48664607 MED4 chr4 54231540 54231714SCFD2 chr3 108205234 108205408 MYH15 chr8 133911011 133911185 TG chr8133935580 133935754 TG chr8 134034228 134034402 TG chr1 246704331246704505 TFB2M chr10 134261298 134261472 C10orf91 chr9 9034352190343695 CTSL1 chr5 26988336 26988510 CDH9 chr7 77756573 77756747 MAGI2chr17 79614896 79615070 TSPAN10 chr9 123877336 123877510 CNTRL chr9127998961 127999135 HSPA5 chr2 166170517 166170691 SCN2A chr2 166172007166172181 SCN2A chr2 225639691 225639865 DOCK10 chr6 34512096 34512270SPDEF chr7 128587279 128587453 IRF5 chr6 7373630 7373804 CAGE1 chr1253553899 53554073 CSAD chr8 42287587 42287761 SLC20A2 chr22 4677290146773075 CELSR1 chr2 1507698 1507872 TPO chr1 175063177 175063351 TNNchr2 107460198 107460372 ST6GAL2 chr18 59894496 59894670 KIAA1468 chr632188161 32188335 NOTCH4 chr17 63221250 63221424 RGS9 chr3 5282221752822391 ITIH1 chr17 40832523 40832697 CCR10 chr11 7660944 7661118PPFIBP2 chr3 77599967 77600141 ROBO2 chr2 10904416 10904590 ATP6V1C2chr5 53606198 53606372 ARL15 chr2 201399752 201399926 SGOL2 chr10102059324 102059498 PKD2L1 chr22 50659438 50659612 TUBGCP6 chr1950461883 50462057 SIGLEC11 chr17 74152259 74152433 RNF157 chr10 59482825948456 FBXO18 chr18 76753167 76753341 SALL3 chr18 76757139 76757313SALL3 chr21 39672105 39672279 KCNJ15 chr15 59752176 59752350 FAM81A chr135480306 35480480 ZMYM6 chr1 197886953 197887127 LHX9 chr17 4215266142152835 G6PC3 chr20 31656606 31656780 BPIFB3 chr2 207621940 207622114MDH1B chr19 6495231 6495405 TUBB4A chr19 6772785 6772959 VAV1 chr2237334110 37334284 CSF2RB chr12 103248999 103249173 PAH chr8 5307150753071681 ST18 chr9 33386941 33387115 AQP7 chr3 125271291 125271466OSBPL11 chr7 48318316 48318491 ABCA13 chr4 73956405 73956581 ANKRD17chr2 235951005 235951182 SH3BP4 chr5 167689431 167689608 ODZ2 chr580409389 80409566 RASGRF2 chr20 76820 76998 DEFB125 chr1 3335467833354858 HPCA chr20 42788454 42788634 JPH2 chr10 27687472 27687652PTCHD3 chr17 3920844 3921024 ZZEF1 chr7 119915387 119915569 KCND2 chr3172165394 172165576 GHSR chr1 197396583 197396765 CRB1 chr3 134851573134851755 EPHB1 chrX 123787437 123787620 ODZ1 chr2 48873703 48873886STON1-GTF2A1L chr4 187510153 187510336 FAT1 chr3 100413608 100413791GPR128 chr11 61511045 61511229 DAGLA chr10 127424274 127424458 C10orf137chr2 136567005 136567189 LCT chr6 56417763 56417947 DST chr7 151845894151846078 MLL3 chr5 110819727 110819912 CAMK4 chr11 49597896 49598085LOC440040 chr12 53039024 53039213 KRT2 chr14 61113057 61113248 SIX1 chr256144829 56145020 EFEMP1 chr19 52569724 52569915 ZNF841 chr8 145059235145059426 PARP10 chr7 77885425 77885617 MAGI2 chr1 152538457 152538650LCE3E chr8 12947763 12947956 DLC1 chrX 134494199 134494392 ZNF449 chr2220099648 220099842 ANKZF1 chr12 53509189 53509383 SOAT2 chr2 4352010943520304 THADA chr16 62055071 62055266 CDH8 chr9 35674150 35674345 CA9chr1 149858596 149858791 HIST2H2AC chr15 73635778 73635974 HCN4 chr1938055893 38056089 ZNF571 chr2 176982002 176982199 HOXD10 chr6 3629786336298060 C6orf222 chr1 151773708 151773907 LINGO4 chr4 158224736158224935 GRIA2 chr6 26031938 26032139 HIST1H3B chr16 71004448 71004649HYDIN chr16 50733557 50733759 NOD2 chr12 12871004 12871206 CDKN1B chr2166535513 166535716 CSRNP3 chr20 2397926 2398129 TGM6 chr18 1185169411851897 CHMP1B chr12 81111035 81111239 MYF5 chr12 125397960 125398165UBC chr12 103696126 103696331 C12orf42 chr2 95537486 95537692 TEKT4 chrX105855747 105855953 CXorf57 chrX 136113602 136113809 GPR101 chr832505636 32505844 NRG1 chr13 29599076 29599285 MTUS2 chr12 5284545052845661 KRT6B chrX 64749506 64749717 LAS1L chr10 93294 93506 TUBB8 chrX57020661 57020873 SPIN3 chr1 12941995 12942208 PRAMEF4 chr19 2199121921991433 ZNF43 chr19 4174679 4174894 SIRT6 chr2 72359489 72359704CYP26B1 chr21 43221549 43221764 PRDM15 chr1 161161088 161161303 ADAMTS4chr5 140249646 140249862 PCDHA11 chr5 140798422 140798638 PCDHGB7 chr2135821632 35821848 KCNE1 chr6 121768857 121769074 GJA1 chr19 5136131151361529 KLK3 chr8 65528283 65528501 CYP7B1 chr6 108882612 108882831FOXO3 chr1 160460935 160461155 SLAMF6 chr11 62996900 62997120 SLC22A25chr10 103988730 103988951 ELOVL3 chr12 14976319 14976541 C12orf60 chr10124390548 124390772 DMBT1 chr22 38273748 38273973 EIF3L chr14 2105211621052341 RNASE11 chrX 152612476 152612702 ZNF275 chr4 38829757 38829984TLR6 chr17 39919262 39919489 JUP chr11 77884984 77885211 KCTD21 chr1109394764 109394992 AKNAD1 chr6 90383966 90384195 MDN1 chr5 175110248175110477 HRH2 chr3 49050041 49050271 WDR6 chr14 71275546 71275777MAP3K9 chr20 31023268 31023501 ASXL1 chr11 104877809 104878042 CASP5chr16 90001761 90001994 TUBB3 chr6 97561811 97562045 KLHL32 chr7 63703366370571 C7orf70 chr16 19883570 19883805 GPRC5B chr4 151504973 151505208MAB21L2 chr8 125989526 125989761 ZNF572 chr7 123152132 123152368 IQUBchr14 102900802 102901038 TECPR2 chr2 105472800 105473037 POU3F3 chr1241966488 41966725 PDZRN4 chr11 6231583 6231820 C11orf42 chr1 119964948119965186 HSD3B2 chr11 6239043 6239281 FAM160A2 chr16 19194848 19195087SYT17 chr6 56483631 56483871 DST chr6 27860666 27860908 HIST1H2AM chr1912244148 12244391 ZNF20 chr11 22646799 22647042 FANCF chr11 1997031019970555 NAV2 chr6 27419768 27420014 ZNF184 chr17 10303801 10304047 MYH8chr16 24582324 24582570 RBBP6 chr1 193038385 193038631 TROVE2 chr4169432916 169433163 PALLD chr6 27777875 27778122 HIST1H3H chr1 216062066216062313 USH2A chr7 31617735 31617985 CCDC129 chr22 38823295 38823545KCNJ4 chr1 70504432 70504683 LRRC7 chr16 53190437 53190690 CHD9 chr1630594010 30594264 ZNF785 chr15 51696681 51696935 GLDN chr1 1108219811082453 TARDBP chr6 32188797 32189052 NOTCH4 chr7 92844714 92844970HEPACAM2 chr10 53458635 53458891 CSTF2T chr10 102763426 102763684 LZTS2chr18 22057169 22057427 HRH4 chr4 118005644 118005904 TRAM1L1 chr5140567090 140568461 PCDHB9 chr2 187626638 187627429 FAM171B chrX34148258 34150182 FAM47A chr3 64084735 64085393 PRICKLE2 chr18 1388463813885323 MC2R chr14 96707139 96707829 BDKRB2 chr17 21318947 21319722KCNJ12 chr5 7867011 7867786 FASTKD3 chrX 78010490 78011286 LPAR4 chr1584651191 84652001 ADAMTSL3 chrX 100911500 100912314 ARMCX2 chr6116599936 116600467 TSPYL1

TABLE 15 Chromosome Start (bp) End (bp) chrX 8138109 8138209 chrX48206376 48206476 chrX 91090521 91090621 chrX 140984443 140984543 chrX151908881 151908981 chrX 153040953 153041053 chrX 153631424 153631524chr1 7724882 7724982 chr1 24083476 24083576 chr1 32841896 32841996 chr134006830 34006930 chr1 155174859 155174959 chr1 230841801 230841901 chr1248737649 248737749 chr2 102083166 102083266 chr2 179398718 179398818chr2 216973855 216973956 chr3 4818945 4819045 chr3 36896651 36896751chr3 38627116 38627216 chr3 49699564 49699664 chr3 52535094 52535194chr3 56627536 56627636 chr3 62355799 62355899 chr3 196529877 196530028chr4 9698387 9698487 chr4 42403164 42403264 chr4 46329605 46329705 chr4114135195 114135295 chr5 94204043 94204143 chr5 167625953 167626053 chr5178540908 178541008 chr6 150385805 150385905 chr7 13894226 13894326 chr744118318 44118418 chr7 64349834 64349934 chr7 128294440 128294540 chr87435213 7435313 chr8 42294507 42294607 chr9 15108 15208 chr9 6984761769847717 chr10 17193296 17193396 chr10 68979399 68979499 chr11 23568492356949 chr11 7982062 7982162 chr11 11987362 11987462 chr11 4903212249032222 chr11 64055365 64055465 chr11 64678071 64678171 chr11 8545667485456774 chr11 117395696 117395796 chr11 117789268 117789417 chr11134605814 134605914 chr12 10659455 10659555 chr12 13719926 13720026chr12 91501855 91501955 chr13 19041955 19042055 chr15 23258090 23258190chr15 28954627 28954727 chr15 42978135 42978235 chr15 85054943 85055043chr15 85788527 85788627 chr16 31272956 31273056 chr16 33783262 33783362chr17 7572917 7573017 chr17 7573926 7574033 chr17 7576510 7576691 chr177576839 7576939 chr17 7577018 7577155 chr17 7577490 7577608 chr177578176 7578289 chr17 7578361 7578554 chr17 7579311 7579590 chr177579660 7579760 chr17 7579825 7579925 chr17 18332963 18333063 chr1741243965 41244164 chr18 180251 180351 chr18 29310984 29311084 chr193011018 3011118 chr19 14091507 14091607 chr19 39421292 39421392 chr1947137855 47137955 chr19 51960611 51960711 chr20 32336701 32336801 chr2033586350 33586450 chr21 10212863 10212968 chr21 42613818 42613918 chr2220710164 20710264 chr22 30803370 30803470 chr22 33559458 33559558 chr2235713817 35713917 track name = 169373_1_OVCA_(—) VIP_P1_tiled_regiondescription = “169373_1_(—) OVCA_VIP_P1_tiled_region” chr1 77248587724997 chr1 24083442 24083603 chr1 32841867 32842012 chr1 3400679734006946 chr1 155174825 155174978 chr1 230841774 230841925 chr1248737627 248737765 chr2 102083143 102083272 chr2 179398692 179398841chr2 216973829 216973909 chr2 216973909 216973998 chr3 4818920 4819066chr3 36896617 36896783 chr3 38627083 38627237 chr3 49699538 49699686chr3 52535072 52535215 chr3 56627509 56627660 chr3 62355774 62355903chr3 196529855 196529933 chr3 196529945 196530052 chr4 42403130 42403288chr4 46329575 46329722 chr5 94204009 94204158 chr5 167625926 167626079chr5 178540874 178541028 chr6 150385770 150385926 chr7 13894204 13894353chr7 44118287 44118446 chr7 64349812 64349948 chr8 42294483 42294582chr10 17193286 17193418 chr10 68979377 68979529 chr11 2356869 2356972chr11 7982041 7982190 chr11 11987341 11987480 chr11 64055338 64055490chr11 64678038 64678188 chr11 85456646 85456787 chr11 117395668117395810 chr11 117789243 117789350 chr11 134605849 134605962 chr1210659427 10659564 chr12 13719897 13720051 chr12 91501825 91501965 chr1542978103 42978261 chr16 31272942 31273080 chr17 7572889 7573037 chr177573894 7574051 chr17 7576519 7576721 chr17 7576809 7576957 chr177576984 7577173 chr17 7577469 7577644 chr17 7578154 7578326 chr177578339 7578589 chr17 7579289 7579605 chr17 7579639 7579772 chr177579804 7579954 chr17 41243941 41244199 chr18 180225 180362 chr1829310950 29311096 chr19 3010990 3011131 chr19 14091474 14091628 chr1939421257 39421413 chr19 47137832 47137963 chr19 51960576 51960733 chr2032336677 32336822 chr20 33586318 33586464 chr21 42613785 42613925 chr2230803344 30803489 chr22 33559434 33559565 chr22 35713793 35713944 chrX8138074 8138152 chrX 91090488 91090640 chrX 140984422 140984566 chrX151908888 151908988 chrX 153040929 153041077 chrX 153631403 153631545

TABLE 16 Chromosome Start (bp) End (bp) chr12 347084 347184 chr12 416903417003 chr12 2064597 2064726 chr12 2760857 2760957 chr12 5603686 5603962chr12 6649648 6649754 chr12 6711158 6711258 chr12 6711541 6711663 chr126858008 6858108 chr12 7061155 7061308 chr12 11139382 11139482 chr1211338798 11338899 chr12 18841022 18841152 chr12 20832994 20833143 chr1221644458 21644558 chr12 25362729 25362845 chr12 25368375 25368494 chr1225378548 25378707 chr12 25380226 25380326 chr12 25398207 25398318 chr1229450060 29450160 chr12 40044040 40044156 chr12 40704272 40704372 chr1246318554 46318654 chr12 46320706 46321116 chr12 49087385 49087485 chr1250452516 50452616 chr12 52715016 52715116 chr12 53097052 53097152 chr1254367341 54367441 chr12 56628940 56629110 chr12 57422538 57422665 chr1257605705 57605805 chr12 57883039 57883139 chr12 57919252 57919352 chr1258024982 58025147 chr12 65856934 65857102 chr12 66531887 66531987 chr1268707423 68707533 chr12 70070695 70070848 chr12 72057233 72057333 chr1272070631 72070776 chr12 72094611 72094775 chr12 88524044 88524197 chr1288566373 88566522 chr12 98921663 98921790 chr12 101680107 101680207chr12 102056179 102056308 chr12 109278860 109278960 chr12 110765384110765516 chr12 111311645 111311765 chr12 111758234 111758479 chr12120595688 120595788 chr12 121017118 121017218 chr12 122812642 122812742chr12 123794256 123794403 chr12 130647686 130648006 chr12 132281685132281785 chr12 133219992 133220146 chr14 20852549 20852667 chr1421861648 21861748 chr14 21961011 21961111 chr14 23312918 23313079 chr1423341479 23341579 chr14 23845008 23845108 chr14 23869951 23870051 chr1424646889 24646989 chr14 24785073 24785421 chr14 31355161 31355271 chr1435331373 35331473 chr14 35592699 35593374 chr14 39871604 39871715 chr1445642257 45642413 chr14 45693598 45693723 chr14 51094835 51094995 chr1453558502 53558650 chr14 60903515 60903615 chr14 74824321 74824464 chr1475514336 75514605 chr14 75590716 75590816 chr14 76112729 76112829 chr1486087922 86089483 chr14 89629100 89629200 chr14 94517547 94517647 chr1494545646 94545824 chr14 100367265 100367376 chr14 101005222 101005322chr14 103599698 103599854 chr14 103996521 103996621 chr14 105174198105174298 chr14 105241988 105242136 chr19 1037596 1037696 chr19 14869481487060 chr19 4329957 4330058 chr19 6222271 6222535 chr19 64772116477311 chr19 7734212 7734330 chr19 10262073 10262221 chr19 1154171811541840 chr19 11618784 11618884 chr19 12384398 12384498 chr19 1243016712430267 chr19 12461691 12461791 chr19 14208172 14208295 chr19 1426207914262179 chr19 16024567 16024667 chr19 16633944 16634044 chr19 1716065717160757 chr19 17943412 17943512 chr19 18376903 18377003 chr19 1842057118420671 chr19 18887987 18888087 chr19 33353366 33353492 chr19 3366637033666470 chr19 34710284 34710384 chr19 35773475 35773575 chr19 3605000836050108 chr19 36050723 36050823 chr19 36053402 36053541 chr19 3605428536054429 chr19 36255934 36256077 chr19 36583590 36583713 chr19 3894813638948270 chr19 38976413 38976523 chr19 39898899 39898999 chr19 3997252239972673 chr19 40711865 40711994 chr19 41063118 41063286 chr19 4275281542753348 chr19 42795774 42795874 chr19 42797189 42797366 chr19 4376615143766251 chr19 43969634 43969734 chr19 44612213 44612313 chr19 4565572045655820 chr19 47572352 47572452 chr19 47883109 47883209 chr19 4793550347935684 chr19 49218062 49218165 chr19 49850446 49850620 chr19 4997170549971805 chr19 49978947 49979058 chr19 50310434 50310534 chr19 5113304951133284 chr19 51189493 51189612 chr19 54327355 54327455 chr19 5454421454544334 chr19 54649645 54649779 chr19 54675698 54675798 chr19 5560741855607546 chr19 55711564 55711664 chr19 55815034 55815194 chr19 5617188156171985 chr19 58083493 58084580 chr19 58596589 58596689 chr22 1937308719373187 chr22 24583181 24583281 chr22 24717388 24717488 chr22 2690613426906234 chr22 29913251 29913355 chr22 30742279 30742379 chr22 3101128731011460 chr22 31535980 31536136 chr22 36689374 36689527 chr22 3669689936696999 chr22 40816886 40817022 chr22 41257113 41257836 chr22 4165038741650487 chr22 41753358 41753458 chr22 42271587 42271687 chr22 4321373443213863 chr22 43218275 43218415 chr22 44083350 44083461 chr22 4455972444559824 chr22 46327193 46327293 chr22 49042408 49042558 chr22 5050686150506984 chr17 4619761 4619861 chr17 4937214 4937374 chr17 63646736364773 chr17 7106511 7106648 chr17 7193549 7193649 chr17 74958197495919 chr17 7572917 7573017 chr17 7573926 7574033 chr17 75765117576691 chr17 7576840 7576940 chr17 7577018 7577155 chr17 75774987577608 chr17 7578176 7578289 chr17 7578361 7578554 chr17 75793107579590 chr17 7579661 7579761 chr17 7579826 7579926 chr17 76066687606768 chr17 7796757 7796857 chr17 7798715 7798815 chr17 78018137801913 chr17 7843413 7843560 chr17 8397050 8397203 chr17 84157718415871 chr17 11650871 11650971 chr17 11924204 11924318 chr17 1195820611958308 chr17 11984673 11984847 chr17 11998892 11999011 chr17 1201110712011226 chr17 12013668 12013768 chr17 12016550 12016677 chr17 1202860012028700 chr17 12032456 12032604 chr17 12043129 12043229 chr17 1204446412044577 chr17 17394656 17394756 chr17 18167754 18167854 chr17 1923286719232967 chr17 21094282 21094382 chr17 26653715 26653815 chr17 2700130027001459 chr17 27027179 27027279 chr17 27889785 27889885 chr17 3248317932483325 chr17 33520308 33520408 chr17 33749443 33749543 chr17 3407710734077207 chr17 37879790 37879913 chr17 37880164 37880264 chr17 3788097837881164 chr17 37881301 37881457 chr17 37881567 37881667 chr17 3788195937882106 chr17 37882813 37882913 chr17 38421173 38421340 chr17 3902287339022973 chr17 39122863 39122963 chr17 40837254 40837354 chr17 4299259142992762 chr17 45219562 45219662 chr17 46622081 46622181 chr17 4843391748434017 chr17 49156944 49157065 chr17 55028068 55028168 chr17 5638991956390037 chr17 56434857 56434957 chr17 56435073 56435352 chr17 5643538256435482 chr17 56435847 56435947 chr17 57247113 57247241 chr17 5966838559668537 chr17 61899082 61899203 chr17 64092676 64092776 chr17 6590570765905807 chr17 67522710 67522850 chr17 71354217 71354343 chr17 7246966272469762 chr17 72943166 72943313 chr17 73239143 73239247 chr17 7348195173482079 chr17 73732131 73732235 chr17 74077962 74078130 chr17 7645899276459133 chr17 78201616 78201759 chr4 661644 661795 chr4 3430284 3430438chr4 3443728 3443845 chr4 4204174 4204305 chr4 10080514 10080625 chr415995602 15995702 chr4 39462409 39462582 chr4 41648459 41648559 chr446060233 46060386 chr4 56336878 56336978 chr4 57179453 57179553 chr470599128 70599228 chr4 71522103 71522203 chr4 76539530 76539630 chr483785564 83785678 chr4 85611658 85611817 chr4 87622492 87622608 chr488344057 88344164 chr4 88986525 88986647 chr4 89381234 89381334 chr490844318 90844423 chr4 105412045 105412145 chr4 106863633 106863733 chr4109784459 109784559 chr4 110756521 110756621 chr4 123302188 123302288chr4 128564867 128564967 chr4 134072527 134073572 chr4 146077068146077168 chr4 168155242 168155342 chr4 169182015 169182140 chr4170926870 170926970 chr4 177100610 177100730 chr4 190873316 190873442chr10 7212889 7213018 chr10 17363164 17363264 chr10 22498435 22498535chr10 24821998 24822166 chr10 26575273 26575423 chr10 27040575 27040675chr10 27964175 27964310 chr10 33018259 33018385 chr10 46969352 46969452chr10 50732090 50732190 chr10 55826516 55826645 chr10 61847978 61848078chr10 63958099 63958199 chr10 64952649 64952749 chr10 70156536 70156638chr10 70182461 70182561 chr10 70509280 70509442 chr10 75673297 75673488chr10 81070738 81070838 chr10 81072398 81072506 chr10 82036208 82036308chr10 93247437 93247537 chr10 93711159 93711323 chr10 96331115 96331215chr10 98336425 98336525 chr10 101558963 101559127 chr10 102107787102107887 chr10 102265117 102265252 chr10 103916945 103917085 chr10104836779 104836930 chr10 105048222 105048322 chr10 105727503 105727653chr10 116062097 116062243 chr10 116444030 116444130 chr10 118424288118424388 chr10 125528116 125528216 chr10 129913954 129914054 chr9732426 732526 chr9 2837246 2837346 chr9 5743663 5743763 chr9 2041427720414380 chr9 21968185 21968285 chr9 21968698 21968798 chr9 2197090021971207 chr9 21974475 21974826 chr9 21994137 21994330 chr9 3242085332421025 chr9 35095211 35095311 chr9 35236465 35236583 chr9 7213102872131128 chr9 77416863 77416963 chr9 77422949 77423090 chr9 9448602594486742 chr9 95077956 95078056 chr9 113166731 113166831 chr9 115969484115969584 chr9 119976940 119977040 chr9 123253584 123253755 chr9124522388 124522509 chr9 129455461 129455561 chr9 130575652 130575823chr9 130580994 130581111 chr9 131022868 131022968 chr9 131591013131591139 chr9 132687193 132687293 chr9 135941916 135942047 chr9136226831 136226931 chr9 137653747 137653847 chr9 139008443 139008679chr9 139397633 139397782 chr9 140056855 140056968 chr9 140218176140218308 chr9 140952471 140952571 chr1 1222151 1222263 chr1 65359906536096 chr1 6727768 6727870 chr1 7811247 7811347 chr1 7838153 7838253chr1 7980889 7980989 chr1 7998252 7998390 chr1 9790591 9790691 chr112052611 12052747 chr1 12785443 12785543 chr1 16264312 16264412 chr119477028 19477128 chr1 20005564 20005664 chr1 22838512 22838612 chr125889552 25889652 chr1 26349532 26349756 chr1 27057828 27057928 chr127087322 27087422 chr1 27088638 27088738 chr1 27088744 27088844 chr127089460 27089560 chr1 27092949 27093049 chr1 27099898 27099998 chr127105786 27106229 chr1 27106416 27106516 chr1 27106566 27106717 chr128800223 28800323 chr1 29475123 29475223 chr1 32196435 32196612 chr134666397 34666547 chr1 35370323 35370423 chr1 38003354 38003454 chr138078426 38078593 chr1 38227550 38227650 chr1 39788581 39788681 chr139878456 39878556 chr1 40713659 40713759 chr1 40756525 40756669 chr143395320 43395420 chr1 44071897 44071997 chr1 45111071 45111171 chr146184874 46185012 chr1 46494439 46494605 chr1 52940825 52941047 chr157378115 57378215 chr1 65306925 65307025 chr1 67390344 67390514 chr170819823 70819923 chr1 74575077 74575237 chr1 74957780 74957950 chr185331663 85331765 chr1 85736461 85736561 chr1 87029343 87029452 chr190470691 90470807 chr1 91967273 91967388 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4125708541257858 chr22 41753325 41753473 chr22 42271561 42271704 chr22 4321370643213880 chr22 43218251 43218423 chr22 44083341 44083474 chr22 4455969144559835 chr22 46327164 46327304 chr22 49042379 49042586 chr22 5050683750507013 chrX 16850722 16850828 chrX 32361221 32361427 chrX 3598978635989933 chrX 37312526 37312680 chrX 47058852 47059024 chrX 5401132554011408 chrX 54011415 54011492 chrX 54578240 54578439 chrX 6947869169478889 chrX 70367786 70367931 chrX 100880079 100880240 chrX 102004344102004417 chrX 111090390 111090535 chrX 112048150 112048367 chrX119072675 119072814 chrX 119694035 119694119 chrX 119694125 119694200chrX 130409129 130409260 chrX 132458481 132458583 chrX 135313677135314212 chrX 135584907 135585121 chrX 142605167 142605243 chrX142803661 142803788 chrX 149937455 149937613 chrX 149984430 149984502chrX 149984530 149984601 chrX 153627898 153627975

TABLE 17 Chromosome Start (bp) End (bp) Gene chr17 47696342 47696470SPOP chr20 29628226 29628331 FRG1B chr9 20414279 20414380 MLLT3 chr1145367713 145367822 NBPF10 chr20 29625872 29625984 FRG1B chr1 145302664145302763 NBPF10 chr10 47207779 47207878 AGAP9 chr3 41266067 41266166CTNNB1 chr17 7577498 7577608 TP53 chr17 47696595 47696747 SPOP chr1912187274 12187476 ZNF844 chr12 132547064 132547163 EP400 chr20 3334569433345793 NCOA6 chr2 207025311 207025434 EEF1B2 chr7 57187717 57187816ZNF479 chr6 45390414 45390513 RUNX2 chr1 74575077 74575237 LRRIQ3 chr1145296361 145296460 NBPF10 chr17 7578403 7578527 TP53 chr14 7127572571275824 MAP3K9 chr7 131241018 131241118 PODXL chr9 127790664 127790763SCAI chr22 43213734 43213863 ARFGAP3 chr10 89692848 89692969 PTEN chr629760313 29760412 LOC554223 chr2 242794863 242794962 PDCD1 chr2209113063 209113162 IDH1 chr6 170871016 170871115 TBP chr11 4778861747788716 FNBP4 chr22 29091697 29091861 CHEK2 chr11 61161352 61161451TMEM216 chr2 107049581 107049680 RGPD3 chr20 46279761 46279860 NCOA3chr4 147560412 147560511 POU4F2 chr22 42538728 42538881 CYP2D7P1 chr3137880694 137880793 DBR1 chr5 156378525 156378748 TIMD4 chrX 128599495128599699 SMARCA1 chr19 12501392 12501650 ZNF799 chr10 52595832 52596044A1CF chr7 127235458 127235735 FSCN3 chr8 23538908 23539136 NKX3-1 chr7154667616 154667768 DPP6 chr11 117863955 117864125 IL10RA chr1 18506121850711 TMEM52 chrX 70349165 70349279 MED12 chr19 13423482 13423595CACNA1A chr7 114269946 114270045 FOXP2 chr19 53958800 53958899 ZNF761chr19 53116795 53116894 ZNF83 chr18 51795916 51796031 POLI chr1740847582 40847681 CNTNAP1 chr7 75130882 75130988 SPDYE5 chr11 102738638102738799 MMP12 chr6 118790284 118790444 CEP85L chr17 42293021 42293177UBTF chr11 106558306 106558448 GUCY1A2 chr12 122812650 122812749 CLIP1chr17 8138395 8138519 CTC1 chr3 12046075 12046174 SYN2 chr12 124171409124171508 TCTN2 chr5 153144021 153144160 GRIA1 chr2 233620932 233621044GIGYF2 chr4 62813829 62813928 LPHN3 chr9 136340557 136340656 SLC2A6 chr6100390835 100390959 MCHR2 chr10 89720772 89720871 PTEN chr1 1288754912887687 PRAMEF11 chr1 152975658 152975782 SPRR3 chrX 151869696151869995 MAGEA6 chr6 26216685 26216865 HIST1H2BG chr1 186275975186276982 PRG4 chr6 54735044 54735358 FAM83B chr10 128973672 128973909FAM196A chr19 7810516 7810836 CD209 chr5 179264556 179264808 C5orf45chr1 75038566 75038907 C1orf173 chr1 237753954 237754211 RYR2 chr1212870802 12871146 CDKN1B chr14 38060571 38061706 FOXA1 chrX 144337190144337334 SPANXN1 chr5 121356087 121356220 SRFBP1 chr12 8290735 8290883CLEC4A chr19 8398906 8399005 KANK3 chr8 135612714 135612813 ZFAT chr731862739 31862845 PDE1C chr20 25436308 25436422 NINL chr8 8912884889128947 MMP16 chr5 52779941 52780041 FST chr8 69002813 69002950 PREX2chr14 24530710 24530809 LRRC16B chr3 124132292 124132391 KALRN chr164910801 4910929 UBN1 chr10 124273706 124273875 HTRA1 chr1 158057795158057944 KIRREL chr4 79792109 79792208 BMP2K chr15 79051762 79051920ADAMTS7 chr15 58306055 58306196 ALDH1A2 chr12 10233905 10234004 CLEC1Achr16 15045627 15045732 NPIP chr4 68719785 68719904 TMPRSS11D chr126608782 26608881 UBXN11 chr12 49434173 49434308 MLL2 chr5 149776098149776197 TCOF1 chr1 44450608 44450707 B4GALT2 chr15 65041611 65041710RBPMS2 chr6 28227391 28227528 NKAPL chr1 240255520 240255619 FMN2 chr1551507266 51507429 CYP19A1 chr7 82784421 82784520 PCLO chr11 108205695108205836 ATM chr6 27114416 27114574 HIST1H2BK chr3 44672553 44672713ZNF197 chr9 79634891 79635215 FOXB2 chr7 82763828 82764232 PCLO chr192917732 2917874 ZNF57 chr19 49926468 49926596 PTH2 chr1 147380241147380400 GJA8 chr19 58384330 58386285 ZNF814 chr11 120008137 120008335TRIM29 chr7 100349723 100350362 ZAN chr1 214556763 214557052 PTPN14 chr8100866041 100866334 VPS13B chr5 427983 428082 AHRR chr6 2602726326027362 HIST1H4B chr1 204409319 204409449 PIK3C2B chr20 1433675 1433785NSFL1C chr9 73152075 73152174 TRPM3 chr11 117374641 117374740 DSCAML1chr17 7788130 7788229 CHD3 chr19 51217054 51217206 SHANK1 chr6 397107397252 IRF4 chr14 105996001 105996100 TMEM121 chr1 149783601 149783719HIST2H2BF chr16 55362954 55363123 IRX6 chr17 16843653 16843775 TNFRSF13Bchr13 19748101 19748254 TUBA3C chr3 10976714 10976885 SLC6A11 chr912775812 12775911 LURAP1L chr19 54646838 54646937 CNOT3 chr1 9196727391967388 CDC7 chr3 178935997 178936122 PIK3CA chr1 160654784 160654893CD48 chr18 8609822 8609921 RAB12 chr6 28554156 28554275 SCAND3 chr3123419028 123419195 MYLK chrX 50350643 50350742 SHROOM4 chr17 75781767578289 TP53 chr10 89717609 89717776 PTEN chr7 101988883 101989029SPDYE6 chr12 52680984 52681083 KRT81 chr19 17943403 17943502 JAK3 chr2057415296 57415471 GNAS chr12 49432197 49432375 MLL2 chr1 156565213156565531 GPATCH4 chr3 49395481 49395680 GPX1 chr15 90320120 90320477MESP2 chr12 49391304 49391666 DDN chr1 149857820 149858039 HIST2H2BEchr1 152636614 152636836 LCE2D chr1 111216033 111217283 KCNA3 chr1718022688 18023608 MYO15A chr14 69256454 69256864 ZFP36L1 chr12 1154614911546791 PRB2 chr2 238274413 238274669 COL6A3 chr11 128781512 128781969KCNJ5 chr17 16593776 16594037 CCDC144A chr5 114466298 114466560 TRIM36chr19 45911703 45911968 CD3EAP chr7 5352527 5352794 TNRC18 chr7 9214665892147137 PEX1 chrX 37026829 37028758 FAM47C chr4 1389053 1389324 CRIPAKchr4 81123290 81123568 PRDM8 chr8 139890022 139890302 COL22A1 chr11124857494 124857795 CCDC15 chr12 66725027 66725339 HELB chr1 1292108912921406 PRAMEF2 chr22 50658758 50659326 TUBGCP6 chr20 39990959 39991281EMILIN3 chr17 18874709 18875038 FAM83G chr1 152127347 152129152 RPTNchr3 128181413 128182005 DNAJB8 chr6 87725480 87726075 HTR1E chr651612954 51613290 PKHD1 chr19 57065051 57065990 ZFP28 chr7 2622495626225295 NFE2L3 chr19 21239843 21240183 ZNF430 chr19 44103049 44103390ZNF576 chr5 45262466 45262808 HCN1 chr11 57077241 57077613 TNKS1BP1 chr9111617061 111618108 ACTL7B chr4 94750558 94750938 ATOH1 chr7 9429324594293632 PEG10 chr7 94293245 94293632 PEG10 chr9 135073410 135073797NTNG2 chr19 53855195 53856762 ZNF845 chr20 49620846 49620945 KCNG1 chr4126242026 126242125 FAT4 chr21 15011835 15011959 POTED chr17 4826404148264185 COL1A1 chr7 43659195 43659322 STK17A chr22 29885743 29885869NEFH chr12 94763686 94763812 CCDC41 chr3 126730814 126730913 PLXNA1 chr889058896 89059012 MMP16 chr2 207425844 207425943 ADAM23 chr15 7501300675013105 CYP1A1 chr13 115047495 115047602 UPF3A chr12 6494392 6494545LTBR chr6 135787137 135787236 AHI1 chr17 39673059 39673216 KRT15 chr1150297400 150297545 PRPF3 chr3 38139234 38139393 DLEC1 chr4 162680562162680683 FSTL5 chr16 2903127 2903248 PRSS22 chr3 186362539 186362709FETUB chr12 57674140 57674313 R3HDM2 chr9 43818040 43818184 CNTNAP3Bchr9 43915811 43915910 CNTNAP3B chr2 141473541 141473671 LRP1B chr1712032455 12032604 MAP2K4 chr18 5397338 5397437 EPB41L3 chr3 9364609393646251 PROS1 chr22 26884093 26884192 SRRD chr19 367064 367224 THEGchr1 224621717 224621816 WDR26 chr1 109823577 109823676 PSRC1 chr1109824257 109824424 PSRC1 chr17 80401692 80401838 C17orf62 chr7 3112653731126636 ADCYAP1R1 chr3 125824596 125824695 ALDH1L1 chrX 5821234 5821333NLGN4X chr14 55818553 55818655 FBXO34 chr12 62777620 62777779 USP15 chr1207818577 207818676 CR1L chr1 207870839 207870938 CR1L chr14 2461975924619858 RNF31 chr1 157103871 157104019 ETV3 chr12 57566909 57567008LRP1 chr5 14711305 14711419 ANKH chr19 47935632 47935731 SLC8A2 chr1631427825 31427967 ITGAD chr4 89618409 89618508 NAP1L5 chr1 5528058655280712 C1orf177 chr1 11090220 11090319 MASP2 chrX 69261657 69261812AWAT2 chr12 104487249 104487348 HCFC2 chr12 64491020 64491155 SRGAP1chr6 152647463 152647581 SYNE1 chr17 34079527 34079626 GAS2L2 chr3122002719 122002818 CASR chr21 45953556 45953655 TSPEAR chr2 220396479220396624 ASIC4 chr9 73235184 73235283 TRPM3 chr2 125521553 125521724CNTNAP5 chrX 148798035 148798187 MAGEA11 chr19 4538279 4538378 LRG1 chr1158063128 158063236 KIRREL chr12 75601571 75601722 KCNC2 chr10 133949433133949580 JAKMIP3 chr5 80548514 80548613 CKMT2 chr3 38755448 38755571SCN10A chr4 20255544 20255643 SLIT2 chr17 65688748 65688847 PITPNC1chr12 104107420 104107546 STAB2 chr17 9739687 9739792 GLP2R chr3127399112 127399211 ABTB1 chr22 24621212 24621382 GGT5 chr1 1297971412979813 PRAMEF8 chr1 27023450 27023622 ARID1A chr1 27057926 27058086ARID1A chr10 15145322 15145421 RPP38 chr2 96795562 96795717 ASTL chr588027590 88027689 MEF2C chr4 26622222 26622321 TBC1D19 chr10 4410406144104188 ZNF485 chr15 89760369 89760468 RLBP1 chr9 113547878 113547977MUSK chr12 117768454 117768562 NOS1 chr8 103564110 103564209 ODF1 chr3180359821 180359981 CCDC39 chr20 47845251 47845425 DDX27 chr16 6787675867876857 THAP11 chr1 181701977 181702076 CACNA1E chr1 181708282181708389 CACNA1E chr1 181767489 181767588 CACNA1E chr19 3604657736046714 ATP4A chr6 89974139 89974238 GABRR2 chr9 33135275 33135376B4GALT1 chr18 47808962 47809109 CXXC1 chr1 63999765 63999887 EFCAB7 chr352412605 52412704 DNAH1 chr4 177058666 177058765 WDR17 chr22 2962824429628391 EMID1 chr11 58391812 58391922 CNTF chr17 7106228 7106327 DLG4chr1 186915768 186915906 PLA2G4A chr6 29910643 29910742 HLA-A chr1740474417 40474516 STAT3 chr11 68822681 68822780 TPCN2 chr20 3387518733875286 FAM83C chr1 24080589 24080745 TCEB3 chr19 10602357 10602518KEAP1 chr12 121471395 121471535 OASL chr2 210993754 210993896 KANSL1Lchr17 7680785 7680923 DNAH2 chrX 70472885 70472984 ZMYM3 chr1 156937778156937916 ARHGEF11 chr12 121434064 121434216 HNF1A chr5 131544933131545032 P4HA2 chr3 112546244 112546343 CD200R1L chr10 2645499526455107 MYO3A chr2 224849584 224849683 SERPINE2 chr12 113748038113748160 SLC24A6 chr14 21960902 21961063 TOX4 chr2 39053657 39053831DHX57 chr1 20005665 20005831 HTR6 chr10 60994116 60994215 PHYHIPL chr1210223942 10224041 CLEC1A chr1 10713917 10714016 CASZ1 chr2 120438864120438963 TMEM177 chr1 10207020 10207148 UBE4B chr1 157558964 157559063FCRL4 chr1 9662257 9662356 TMEM201 chr2 67631942 67632041 ETAA1 chr10105798163 105798286 COL17A1 chr19 19136340 19136439 SUGP2 chr19 2080799820808097 ZNF626 chr22 24982242 24982341 FAM211B chr9 78686641 78686814PCSK5 chr8 104340495 104340644 FZD6 chr1 159033262 159033361 AIM2 chr1461115589 61115688 SIX1 chr1 76397666 76397765 ASB17 chr5 5547197855472109 ANKRD55 chr21 28327057 28327190 ADAMTS5 chr20 60448783 60448956CDH4 chr3 154042007 154042106 DHX36 chr1 148594406 148594508 NBPF15chr20 44004087 44004186 TP53TG5 chr12 31237902 31238060 DDX11 chrX96139971 96140070 RPA4 chr22 47095209 47095362 CERK chr11 134253690134253789 B3GAT1 chr17 80121075 80121233 CCDC57 chr5 180651193 180651292TRIM41 chr4 69870619 69870718 UGT2B10 chr1 12726521 12726654 AADACL4chr4 184567615 184567714 RWDD4 chr5 37038703 37038840 NIPBL chr1940367791 40367890 FCGBP chr1 159683404 159683568 CRP chr20 6152518561525284 DIDO1 chr17 10216499 10216635 MYH13 chrX 70348447 70348568MED12 chr17 10404708 10404807 MYH1 chr2 238737977 238738076 RBM44 chr1952537921 52538072 ZNF432 chr8 109001323 109001422 RSPO2 chrX 8341108283411199 RPS6KA6 chr22 19197945 19198044 CLTCL1 chr1 153084995 153085094SPRR2F chr18 55992227 55992394 NEDD4L chr19 24310250 24310349 ZNF254chr17 40328126 40328225 KCNH4 chr19 8188347 8188473 FBN3 chr4 7395101873951117 ANKRD17 chr6 146480622 146480721 GRM1 chr6 146755621 146755764GRM1 chr17 56688554 56688675 TEX14 chr1 32050486 32050637 TINAGL1 chr1620335227 20335326 GP2 chr12 46246099 46246242 ARID2 chr17 1264764312647742 MYOCD chr8 41456658 41456823 AGPAT6 chr1 47904197 47904296FOXD2 chr19 10088269 10088377 COL5A3 chr6 33137159 33137267 COL11A2 chr1220253068 220253188 BPNT1 chr1 75072284 75072383 C1orf173 chr2 130832499130832651 POTEF chr2 119915150 119915249 C1QL2 chr9 116858330 116858487KIF12 chr4 190873316 190873442 FRG1 chr4 190878552 190878657 FRG1 chr12667673 667827 B4GALNT3 chr2 176981838 176981937 HOXD10 chr2 9659294896593047 ANKRD36C chr12 52863561 52863660 KRT6C chr20 1902225 1902324SIRPA chr6 132910415 132910514 TAAR5 chr16 314894 314993 ITFG3 chr825745357 25745488 EBF2 chr1 158585064 158585164 SPTA1 chr1 158615284158615384 SPTA1 chr1 158637744 158637843 SPTA1 chr1 158639487 158639586SPTA1 chr6 85446535 85446704 TBX18 chr19 48620894 48621038 LIG1 chr1108023224 108023323 NTNG1 chr16 89703633 89703763 DPEP1 chr19 35894403589553 GIPC3 chr4 73186430 73186587 ADAMTS3 chr11 803338 803451 PIDDchr1 183515217 183515316 SMG7 chr19 46020920 46021092 VASP chr4189060920 189061024 TRIML1 chr18 72228090 72228253 CNDP1 chr10 3743071437430813 ANKRD30A chr10 37508446 37508620 ANKRD30A chr12 4941638349416482 MLL2 chr4 103528306 103528476 NFKB1 chr7 20824878 20824977 SP8chr7 130357573 130357716 TSGA13 chr22 19119463 19119562 TSSK2 chr11864424 864523 TSPAN4 chr20 3802816 3802915 AP5S1 chr11 58892327 58892426FAM111B chr4 3443728 3443845 HGFAC chr1 156877401 156877522 PEAR1 chr1619041545 19041691 TMC7 chr16 19058398 19058497 TMC7 chr19 4058050240580601 ZNF780A chr6 34985544 34985689 ANKS1A chr15 65502013 65502112CILP chr13 113825968 113826067 PROZ chrX 150156249 150156387 HMGB3 chr171198784 1198917 TUSC5 chr8 134488065 134488182 ST3GAL1 chr17 7273690672737017 RAB37 chr9 139847343 139847480 LCN12 chr2 55461966 55462098RPS27A chr8 139277945 139278085 FAM135B chr1 207112652 207112751 PIGRchr2 206562233 206562332 NRP2 chr18 25565571 25565670 CDH2 chr1825572607 25572706 CDH2 chr12 76740913 76741012 BBS10 chr18 2909976529099900 DSG2 chr10 75104806 75104905 TTC18 chr5 179564960 179565059RASGEF1C chr5 72419528 72419627 TMEM171 chr7 138764409 138764547 ZC3HAV1chr17 37369247 37369385 STAC2 chr9 139277946 139278045 SNAPC4 chr320219751 20219850 SGOL1 chr19 58352925 58353024 ZNF587B chr10 5083568550835784 CHAT chr18 21662876 21663045 TTC39C chr6 123319045 123319144CLVS2 chr6 123369766 123369877 CLVS2 chr16 67199621 67199749 HSF4 chr2100055091 100055190 REV1 chr15 66853341 66853440 LCTL chr14 2404035824040526 JPH4 chr11 47198066 47198185 ARFGAP2 chr16 72162964 72163132PMFBP1 chr8 2830669 2830768 CSMD1 chr8 3000013 3000112 CSMD1 chr1543714099 43714238 TP53BP1 chr2 128046320 128046419 ERCC3 chr2 128050179128050278 ERCC3 chr2 165365251 165365362 GRB14 chr1 12331050 12331181VPS13D chr6 31930215 31930362 SKIV2L chr14 23651972 23652123 SLC7A8chr16 29820927 29821026 MAZ chr12 121176620 121176719 ACADS chr1210796869 210797014 HHAT chr2 137814398 137814556 THSD7B chr7 149518485149518638 SSPO chr20 61288117 61288287 SLCO4A1 chr7 4824538 4824679AP5Z1 chr1 165370499 165370647 RXRG chr1 5964710 5964829 NPHP4 chr1689799877 89799976 ZNF276 chrX 148037315 148037414 AFF2 chr11 2670260126702768 SLC5A12 chr15 101528887 101528986 LRRK1 chr2 88425694 88425867FABP1 chr10 97192204 97192333 SORBS1 chr11 47744540 47744639 FNBP4 chr1322246171 22246273 FGF9 chr8 52359563 52359722 PXDNL chr4 110972750110972849 ELOVL6 chr12 63543656 63543829 AVPR1A chr10 7780583 7780721ITIH2 chr16 67913752 67913874 EDC4 chr8 142222364 142222493 SLC45A4 chr5156592677 156592776 FAM71B chr9 71628893 71628992 PRKACG chr19 46828304682929 LOC100131094 chr11 92568127 92568226 FAT3 chr11 9257709692577195 FAT3 chr2 145156348 145156447 ZEB2 chr12 108603924 108604023WSCD2 chr11 4130853 4130952 RRM1 chr1 94487401 94487507 ABCA4 chr1774732289 74732457 SRSF2 chr15 52433336 52433469 GNB5 chr19 4409934844099447 IRGQ chr19 57089368 57089491 ZNF470 chr11 122774655 122774754C11orf63 chr22 16287547 16287674 POTEH chr10 73461839 73461938 CDH23chr10 73574933 73575032 CDH23 chr14 72976861 72976987 RGS6 chr1493708963 93709062 BTBD7 chr6 132195392 132195491 ENPP1 chr14 103442219103442386 CDC42BPB chr12 52200654 52200822 SCN8A chr11 116718191116718324 SIK3 chr16 85936621 85936795 IRF8 chr15 49036436 49036540CEP152 chr22 40816858 40816957 MKL1 chr2 80874757 80874856 CTNNA2 chr49828029 9828128 SLC2A9 chr4 9982215 9982361 SLC2A9 chr4 141543403141543502 TBC1D9 chrX 125685873 125685972 DCAF12L1 chr20 3214796 3214895SLC4A11 chr3 26751542 26751672 LRRC3B chr10 88277653 88277752 WAPAL chr4158253970 158254138 GRIA2 chr12 4479821 4479920 FGF23 chr10 7864706978647237 KCNMA1 chr19 19729319 19729438 PBX4 chr1 46751084 46751183LRRC41 chr22 37263419 37263518 NCF4 chr17 19235212 19235311 EPN2 chr1575941774 75941873 SNX33 chr9 99521361 99521460 ZNF510 chr12 5801404758014190 SLC26A10 chr9 77448913 77449038 TRPM6 chr5 130840318 130840471RAPGEF6 chr17 5009529 5009628 ZNF232 chr15 48431290 48431389 SLC24A5chr1 70687321 70687420 SRSF11 chr3 44700524 44700679 ZNF35 chr1160785253 60785404 CD6 chr1 97564044 97564188 DPYD chr9 94172204 94172303NFIL3 chr16 74490546 74490653 GLG1 chr3 111426771 111426941 PLCXD2 chr1130034012 30034111 KCNA4 chr1 156779027 156779126 SH2D2A chr5 175110214175110313 HRH2 chr6 168430261 168430360 KIF25 chr1 43296636 43296735ERMAP chr6 134212845 134212944 TCF21 chr20 42747144 42747263 JPH2 chr2042788362 42788461 JPH2 chr2 219735781 219735880 WNT6 chr2 171862656171862792 TLK1 chr2 45233390 45233550 SIX2 chr8 113308061 113308235CSMD3 chr16 28883169 28883268 SH2B1 chr13 21417908 21418025 XPO4 chr1914877046 14877193 EMR2 chr1 35915958 35916110 KIAA0319L chr1 215848612215848711 USH2A chr16 29996643 29996742 TAOK2 chrX 151138728 151138827GABRE chr20 3686553 3686652 SIGLEC1 chr20 46301026 46301125 SULF2 chr1915794434 15794533 CYP4F12 chr19 23927261 23927360 ZNF681 chrX 2997263829972809 IL1RAPL1 chr1 55521742 55521841 PCSK9 chr19 18976420 18976575UPF1 chr1 160466058 160466157 SLAMF6 chr6 6224961 6225060 F13A1 chr1152800111 152800256 LCE1A chr7 33312673 33312772 BBS9 chr6 4289730842897459 CNPY3 chr5 139939910 139940032 APBB3 chr11 43345020 43345119API5 chr18 14513660 14513784 POTEC chr22 23523994 23524103 BCR chr2129026304 129026420 HS6ST1 chr17 1559941 1560055 PRPF8 chr6 155743827155743990 NOX3 chr1 44595137 44595236 KLF17 chr3 111795744 111795843TMPRSS7 chr3 137483866 137483993 SOX14 chr4 42895344 42895493 GRXCR1chr4 170613374 170613473 CLCN3 chr10 113920449 113920548 GPAM chr754617680 54617802 VSTM2A chr3 168819847 168820014 MECOM chr17 7818797078188127 SGSH chr19 56459465 56459564 NLRP8 chr19 56466870 56466969NLRP8 chr3 151148077 151148176 MED12L chr6 36651924 36652023 CDKN1A chr154359977 54360076 DIO1 chr1 109803674 109803773 CELSR2 chr16 25065732506672 CCNF chr8 133196487 133196614 KCNQ3 chr17 10350361 10350462 MYH4chr1 177030339 177030438 ASTN1 chr7 75053808 75053911 POM121C chr1211461577 11461676 PRB4 chr1 16534594 16534703 ARHGEF19 chr9 57634935763655 KIAA1432 chr1 64608117 64608216 ROR1 chr9 18504826 18504954ADAMTSL1 chr7 140453074 140453193 BRAF chr7 140481375 140481493 BRAFchr11 65325188 65325329 LTBP3 chr22 23959753 23959852 C22orf43 chr748266858 48267022 ABCA13 chr7 48314998 48315097 ABCA13 chr7 4850654748506646 ABCA13 chr1 152748923 152749022 LCE1F chr3 49136770 49136869QARS chr17 4805226 4805382 CHRNE chr5 14504502 14504650 TRIO chr117599834 17599942 PADI3 chr10 73050799 73050898 UNC5B chr18 32150863215185 MYOM1 chr6 136913310 136913479 MAP3K5 chr4 9783978 9784077 DRD5chr19 7670147 7670246 CAMSAP3 chr3 17413566 17413739 TBC1D5 chr3122459485 122459584 HSPBAP1 chr10 25160909 25161008 PRTFDC1 chr1936278554 36278653 ARHGAP33 chr12 7080183 7080282 EMG1 chr22 3748076637480879 TMPRSS6 chr4 46067416 46067561 GABRG1 chr8 101612573 101612672SNX31 chr8 102570741 102570840 GRHL2 chr13 95862940 95863039 ABCC4 chr1147360070 47360230 MYBPC3 chr1 114442764 114442863 AP4B1 chr15 7829058578290684 TBC1D2B chr15 78290571 78290670 TBC1D2B chr14 94004483 94004582UNC79 chr8 20107495 20107642 LZTS1 chr1 74649255 74649354 LRRIQ3 chr1741957219 41957318 MPP2 chr1 43783549 43783648 TIE1 chr6 2627141926271518 HIST1H3G chr21 30339198 30339297 LTN1 chr19 58132377 58132476ZNF134 chr2 218683367 218683466 TNS1 chr5 125919614 125919713 ALDH7A1chr5 159680529 159680628 CCNJL chr18 48591845 48591944 SMAD4 chr113380509 3380679 ZNF195 chr8 131861854 131861953 ADCY8 chr19 5856501658565115 ZSCAN1 chr1 29475170 29475269 SRSF4 chr2 219353026 219353144USP37 chr19 803548 803647 PTBP1 chr1 35578983 35579082 ZMYM1 chr189206759 89206858 PKN2 chr17 61557709 61557836 ACE chr16 8574376985743913 C16orf74 chr19 39995871 39995970 DLL3 chr3 112710021 112710120GTPBP8 chr1 57173292 57173391 PRKAA2 chr6 34824015 34824186 UHRF1BP1chr8 145109714 145109816 OPLAH chr1 231299623 231299722 TRIM67 chr1155887289 155887463 KIAA0907 chr16 3658438 3658537 SLX4 chr9 139091592139091726 LHX3 chr3 178916856 178916955 PIK3CA chr3 178921503 178921602PIK3CA chr3 178952018 178952117 PIK3CA chr1 145327492 145327665 NBPF10chr1 145359014 145359187 NBPF10 chr1 145368502 145368605 NBPF10 chr43234944 3235043 HTT chr11 20622882 20622995 SLC6A5 chr7 3990554 3990666SDK1 chr22 21354935 21355076 THAP7 chr15 53992045 53992144 WDR72 chr9125895123 125895247 STRBP chr11 6261369 6261468 CNGA4 chr8 4152985641529955 ANK1 chr17 72915872 72915971 USH1G chr 19 40331053 40331155 FBLchr7 95705368 95705509 DYNC1I1 chr22 44083350 44083461 EFCAB6 chr1021962565 21962664 MLLT10 chr12 15742388 15742524 PTPRO chr1 4468039544680510 DMAP1 chr8 25174522 25174647 DOCK5 chr6 17507399 17507543 CAP2chr18 65179095 65179194 DSEL chr1 158325230 158325329 CD1E chr2242039084 242039187 MTERFD2 chr1 6228209 6228337 CHD5 chr7 139285208139285307 HIPK2 chr10 81072398 81072506 ZMIZ1 chr9 137657503 137657602COL5A1 chr7 141672536 141672670 TAS2R38 chr9 72755060 72755204 MAMDC2chr7 148701023 148701136 PDIA4 chr5 149460466 149460565 CSF1R chr1233190095 233190194 PCNXL2 chr17 42390778 42390877 RUNDC3A chr1666436886 66436985 CDH5 chr1 151340674 151340795 SELENBP1 chr16 5584442855844576 CES1 chr16 70287821 70287941 AARS chr20 42265784 42265893 IFT52chr17 42333040 42333214 SLC4A1 chr19 22836719 22836818 ZNF492 chr2118924171 18924270 CXADR chr12 58022501 58022637 B4GALNT1 chr12 5802498258025147 B4GALNT1 chr17 79428858 79428957 BAHCC1 chr16 5038149 5038281SEC14L5 chr17 17250118 17250261 NT5M chr6 33177763 33177862 RING1 chr167568192 7568291 RBFOX1 chr16 7645547 7645646 RBFOX1 chr4 3923017639230275 WDR19 chr14 19553528 19553627 POTEG chr7 141952291 141952431PRSS58 chr19 49646062 49646161 PPFIA3 chr17 3917383 3917482 ZZEF1 chr476813003 76813131 PPEF2 chr4 1805418 1805563 FGFR3 chr7 151845676151845775 MLL3 chr11 132177582 132177717 NTM chr1 112318708 112318871KCND3 chr9 35906509 35906608 HRCT1 chr2 215645754 215645853 BARD1 chr355508371 55508481 WNT5A chr10 26575273 26575423 GAD2 chr11 7792469177924859 USP35 chr8 48771409 48771547 PRKDC chr10 55587148 55587308PCDH15 chr11 111591255 111591354 SIK2 chr3 140406822 140406921 TRIM42chr10 43288403 43288533 BMS1 chr4 1643035 1643134 FAM53A chr3 5573340555733540 ERC2 chr1 94054820 94054919 BCAR3 chr22 42264673 42264772SREBF2 chr3 27763357 27763456 EOMES chr19 44564607 44564734 ZNF223 chr1253238342 53238507 KRT78 chr4 62903448 62903574 LPHN3 chr12 113321097113321207 RPH3A chr19 43439763 43439888 PSG7 chr7 8125954 8126053 GLCCI1chr7 99689042 99689143 COPS6 chr7 22985617 22985716 FAM126A chr177340254 7340353 TMEM102 chr7 39610104 39610226 YAE1D1 chr10 6014842960148579 TFAM chr14 20846219 20846389 TEP1 chr20 9364886 9364985 PLCB4chr7 129664105 129664204 ZC3HC1 chr1 34102076 34102175 CSMD2 chr1051754122 51754221 AGAP6 chr10 106976741 106976840 SORCS3 chr16 630857630972 PIGQ chr4 13610153 13610292 BOD1L1 chr18 54591200 54591299 WDR7chr12 86373503 86373602 MGAT4C chr12 54903631 54903769 NCKAP1L chr2229890606 229890760 PID1 chr8 66753605 66753743 PDE7A chr2 158272195158272363 CYTIP chr11 118986781 118986880 C2CD2L chr1 35370016 35370115DLGAP3 chr7 76029671 76029806 SRCRB4D chr11 8737282 8737381 ST5 chr1917394176 17394285 ANKLE1 chr11 108788586 108788685 DDX10 chr10 103906597103906696 PPRC1 chr3 51864402 51864501 IQCF3 chr15 101425471 101425576ALDH1A3 chr17 7576839 7576938 TP53 chr17 7577018 7577155 TP53 chr172290808 2290907 MNT chr17 38906741 38906840 KRT25 chr1 24019098 24019249RPL11 chr20 34312491 34312644 RBM39 chr2 99182102 99182229 INPP4A chr1494395228 94395393 FAM181A chr19 19337518 19337626 NCAN chr22 3687668636876785 TXN2 chr2 169417701 169417832 CERS6 chr13 47409097 47409211HTR2A chr22 37962525 37962624 CDC42EP1 chr1 38197082 38197254 EPHA10chr7 796442 796544 HEATR2 chr1 147230938 147231037 GJA5 chr17 2738049927380598 PIPOX chr17 58700833 58700932 PPM1D chr3 11744426 11744525VGLL4 chr16 17221521 17221620 XYLT1 chr11 14808043 14808142 PDE3B chr4155241970 155242139 DCHS2 chr4 155298431 155298530 DCHS2 chr4 8804724388047342 AFF1 chr4 156643189 156643348 GUCY1A3 chr3 47125462 47125561SETD2 chrX 140969360 140969485 MAGEC3 chr17 27999050 27999149 SSH2 chr5141304974 141305092 KIAA0141 chr6 35927487 35927586 SLC26A8 chr9101748265 101748364 COL15A1 chr1 65147687 65147789 CACHD1 chr19 4998216549982304 FLT3LG chr22 36689374 36689527 MYH9 chr1 196658587 196658726CFH chr1 196709748 196709922 CFH chr16 767059 767158 METRN chr7 1868812218688221 HDAC9 chr3 38038998 38039125 VILL chr3 38047325 38047429 VILLchr7 96635388 96635548 DLX6 chr3 149686218 149686317 PFN2 chr1 11483711148473 TNFRSF4 chr4 47427806 47427905 GABRB1 chr1 45974587 45974686MMACHC chr6 50740404 50740503 TFAP2D chr6 63990280 63990379 LGSN chr297464793 97464963 CNNM4 chr12 6125254 6125398 VWF chr12 6161822 6161937VWF chr10 12131091 12131190 DHTKD1 chr6 123127377 123127502 SMPDL3A chr1228433168 228433267 OBSCN chr1 228466364 228466463 OBSCN chr21 2784081927840950 CYYR1 chr1 207133023 207133142 FCAMR chr9 140086589 140086702TPRN chr4 8221074 8221173 SH3TC1 chr13 24895747 24895846 C1QTNF9 chr1339357206 39357332 FREM2 chr3 33552112 33552239 CLASP2 chr3 3360229933602463 CLASP2 chr21 42843752 42843851 TMPRSS2 chr12 49724259 49724358TROAP chr5 137721935 137722058 KDM3B chr5 153830650 153830773 SAP30Lchr17 56060582 56060681 VEZF1 chr20 24523909 24524046 SYNDIG1 chr2239909947 39910046 SMCR7L chr1 44878071 44878185 RNF220 chr16 4663827046638369 SHCBP1 chr9 117852944 117853043 TNC chr11 94924621 94924720SESN3 chr2 133489317 133489416 NCKAP5 chr17 38938514 38938671 KRT27 chr98449724 8449845 PTPRD chr7 100210469 100210602 MOSPD3 chr7 107580611107580710 LAMB1 chr10 134261380 134261479 C10orf91 chrX 9966236399662462 PCDH19 chr2 241831081 241831180 C2orf54 chr1 151105832151105931 SEMA6C chr17 6941869 6941968 SLC16A13 chr16 68855983 68856089CDH1 chr20 61981680 61981809 CHRNA4 chr12 11506565 11506670 PRB1 chr1780398916 80399062 HEXDC chr17 56083168 56083267 SRSF1 chr20 1376331713763444 ESF1 chr7 84628810 84628963 SEMA3D chr3 167747601 167747700GOLIM4 chr11 62444224 62444335 UBXN1 chr14 63453789 63453905 KCNH5 chr9138395790 138395889 MRPS2 chr11 115080285 115080384 CADM1 chr9 1312175113121850 MPDZ chr17 34945767 34945866 GGNBP2 chr12 121134117 121134216MLEC chr6 126080680 126080849 HEY2 chr9 112184997 112185132 PTPN3 chr1473719356 73719483 PAPLN chr11 46917760 46917886 LRP4 chr6 3272662532726775 HLA-DQB2 chr16 31498976 31499075 SLC5A2 chr18 51013166 51013328DCC chr17 47302341 47302440 PHOSPHO1 chr18 76755211 76755310 SALL3 chr1051584790 51584889 NCOA4 chr19 7585057 7585156 ZNF358 chr19 3857275638572931 SIPA1L3 chr12 118198838 118199237 KSR2 chr6 27806475 27806652HIST1H2BN chr1 190129809 190129986 FAM5C chr12 115109859 115110036 TBX3chr6 3850336 3850736 FAM50B chr11 116728636 116729351 SIK3 chr1240370882 240371602 FMN2 chr19 16860826 16861006 NWD1 chr6 3205365332053833 TNXB chr9 27948976 27949701 LINGO2 chr10 48389661 48390798 RBP3chr16 3778252 3778982 CREBBP chr5 66458986 66459168 MAST4 chr13 4514851545148697 TSC22D1 chr13 29599442 29600585 MTUS2 chr9 138714197 138714932CAMSAP1 chr3 187447231 187447646 BCL6 chr5 143586926 143587110 KCTD16chr1 152082219 152082960 TCHH chr8 81897132 81897550 PAG1 chr5 35996053600023 IRX1 chr4 71472191 71472378 AMBN chr1 203316604 203316791 FMODchr9 118950295 118950482 PAPPA chr7 23207466 23207657 KLHL7 chr192226422 2226858 DOT1L chr19 44515337 44515532 ZNF230 chr3 7866686278667057 ROBO1 chr9 37740676 37740872 FRMPD1 chr19 3600347 3600543TBXA2R chr19 53762189 53762386 VN1R2 chr18 56246149 56246942 ALPK2 chr2238822858 38823056 KCNJ4 chr19 51628274 51628472 SIGLEC9 chr11 128844093128844291 ARHGAP32 chr2 128262414 128262863 IWS1 chr17 37627428 37627878CDK12 chr2 233246233 233246433 ALPP chr15 89386656 89386856 ACAN chr1249484962 49485162 DHH chr7 148979000 148979202 ZNF783 chr19 5046193650462139 SIGLEC11 chr4 146058803 146059007 OTUD4 chr18 74091046 74091251ZNF516 chr20 278687 279151 ZCCHC3 chr7 41739652 41739858 INHBA chr3147128329 147128794 ZIC1 chr3 49697948 49698155 BSN chr14 2487708924877296 NYNRIN chr4 41648507 41648714 LIMCH1 chr4 146823319 146824153ZNF827 chr6 54805204 54805412 FAM83B chr1 237947226 237947436 RYR2 chr113183307 13183781 LOC440563 chr2 177036308 177036520 HOXD3 chr1093999535 93999748 CPEB3 chr1 12907827 12908040 LOC649330 chr16 5248418952484403 TOX3 chr1 152382358 152382842 CRNN chr8 59409195 59409410CYP7A1 chr9 108424811 108425026 TAL2 chr13 115090480 115090967 CHAMP1chr7 2583248 2583465 BRAT1 chr5 44388498 44388718 FGF10 chr14 3713244337132663 PAX9 chr2 85554392 85554613 TGOLN2 chr12 57920424 57920646 MBD6chr8 110980472 110980696 KCNV1 chr3 50332155 50332379 HYAL3 chr1158064570 158064795 KIRREL chr17 46805441 46805667 HOXB13 chr7 4831829348318520 ABCA13 chr5 137680779 137681006 FAM53C chr5 140604527 140605446PCDHB14 chr12 86373541 86374059 MGAT4C chr1 18023591 18023821 ARHGEF10Lchr7 123301994 123302928 LMOD2 chr16 9857804 9858738 GRIN2A chr1205238669 205238902 TMCC2 chr8 1497601 1497835 DLGAP2 chr3 150931785150932019 P2RY14 chr5 148406690 148407224 SH3TC2 chr8 88885072 88886041DCAF4L2 chr6 66205044 66205286 EYS chr19 56089907 56090152 ZNF579 chr1630456027 30456580 SEPHS2 chr2 167262387 167262941 SCN7A chr4 7014623470146413 UGT2B28 chr7 149430770 149431016 KRBA1 chr5 43039708 43039954ANXA2R chr4 158257611 158257857 GRIA2 chr18 19153403 19154391 ESCO1 chr170503848 70504095 LRRC7 chr14 26917260 26917507 NOVA1 chr1 6714779567148042 SGIP1 chr6 69348839 69349087 BAI3 chr3 148458895 148459145AGTR1 chr16 75690149 75690400 TERF2IP chr14 51132077 51132329 SAV1 chr457181438 57182008 KIAA1211 chr17 7139748 7140002 PHF23 chr19 5291915552919411 ZNF528 chr18 9887073 9888100 TXNDC2 chr5 148747556 148747814PCYOX1L chr3 38592386 38592969 SCN5A chr22 41573199 41573978 EP300 chr1751900687 51901273 KIF2B chr14 93649655 93649915 MOAP1 chr18 54160065416267 EPB41L3 chr5 45645336 45645597 HCN1 chr22 30688487 30688749TBC1D10A chr11 19077544 19077807 MRGPRX2 chr16 87451065 87451329 ZCCHC14chr3 194081182 194081449 LRRC15 chr19 12155152 12155758 ZNF878 chr161129641 1129912 SSTR5 chr9 100970982 100971253 TBC1D2 chr5 130766662130766934 RAPGEF6 chr12 57485186 57485458 NAB2 chr20 62839352 62839625MYT1 chr2 133540001 133540275 NCKAP5 chr17 30348866 30349141 LRRC37Bchr2 136569966 136570243 LCT chr1 180885313 180885942 KIAA1614 chr373111480 73111760 EBLN2 chr5 140563060 140563341 PCDHB16 chr11 130343147130343429 ADAMTS15 chr7 30491365 30491789 NOD1 chr7 72891713 72891996BAZ1B chr5 63256284 63256568 HTR1A chr5 139060649 139060934 CXXC5 chr6100841375 100841664 SIM1 chr1 235345028 235345318 ARID4B chr8 7348014473480434 KCNB2 chr9 104238561 104239215 TMEM246 chr16 24372742 24373179CACNG3 chr5 159992484 159992775 ATP10B chr1 112524315 112524976 KCND3chr3 124951821 124952116 ZNF148 chr13 84454722 84455387 SLITRK1 chr1937677024 37677691 ZNF585B chr14 23828654 23828952 EFS chr16 32994683299766 MEFV chr10 88768853 88769151 AGAP11 chr3 151545614 151545912AADAC chr6 143074326 143074627 HIVEP2 chr4 77818328 77818630 SOWAHBchr15 33261112 33261414 FMN1 chr1 203134668 203134970 ADORA1 chr1226924201 226924885 ITPKB chr20 16359635 16360553 KIF16B chr17 73660467366352 ZBTB4 chr7 89856338 89856644 STEAP2 chr18 8824762 8825068 SOGA2chr2 237489180 237489880 CXCR7 chr3 184910073 184910385 EHHADH chr16830488 830800 MSLNL chr12 47471281 47471985 AMIGO2 chrX 129518742129519056 GPR119 chr5 140589805 140590278 PCDHB12 chr1 227152756227153071 ADCK3 chr6 7229900 7230612 RREB1 chr1 207195319 207195635C1orf116 chr19 52272404 52272720 FPR2 chr1 12855600 12855917 PRAMEF1chr1 149884959 149885277 SV2A chr14 94088049 94088368 UNC79 chr1946375371 46375690 FOXA3 chr19 38976305 38976784 RYR1 chr19 5010280850103129 PRR12 chr10 52103413 52103737 SGMS1 chr1 169510827 169511558 F5chr17 74392305 74392630 UBE2O chr7 110763904 110764638 LRRN3 chr1739967832 39968158 LEPREL4 chr1 74507070 74507398 LRRIQ3 chr13 4170543941706179 KBTBD6 chr12 13716715 13717458 GRIN2B chr6 76660410 76660740IMPG1 chr16 83998850 83999181 OSGIN1 chr11 118498112 118498443 PHLDB1chr3 39229796 39230129 XIRP1 chr2 219507558 219507894 ZNF142 chr1254756830 54757588 GPR84 chr2 99012645 99012982 CNGA3 chr13 5182591351826252 FAM124A chr3 101383902 101384242 ZBTB11 chr2 80529774 80530544LRRTM1 chr6 128134392 128134735 THEMIS chr4 155506851 155507195 FGA chr3119133913 119134257 ARHGAP31 chr19 49206415 49207195 FUT2

TABLE 18 Chromosome Start (bp) End (bp) Gene chr7 140453046 140453220BRAF chr1 115256441 115256615 NRAS chr9 21971015 21971189 CDKN2A chr7142459644 142459861 PRSS1 chr17 11666754 11666928 DNAH9 chr5 1376609513766269 DNAH5 chr14 19553511 19553826 POTEG chr1 241261926 241262100RGS7 chr16 67694127 67694301 ACD chr20 41306559 41306779 PTPRT chr1199690275 99690470 CNTN5 chr2 141777477 141777651 LRP1B chr2 107049548107049722 RGPD3 chr8 121381559 121381733 COL14A1 chr1 153177243153177438 LELP1 chr1 176915070 176915251 ASTN1 chr19 43699170 43699391PSG4 chr3 38949439 38949613 SCN11A chr2 138169212 138169386 THSD7B chr1068940056 68940230 CTNNA3 chr5 13864513 13864742 DNAH5 chr8 131916026131916289 ADCY8 chr4 47746422 47746596 CORIN chr1 179620032 179620206TDRD5 chr19 57666600 57666774 DUXA chr5 101834205 101834544 SLCO6A1 chr657512513 57512695 PRIM2 chr21 41648023 41648197 DSCAM chr8 30812323081406 CSMD1 chr12 122812612 122812786 CLIP1 chr7 140481347 140481521BRAF chr10 89692828 89693004 PTEN chr18 14542652 14543063 POTEC chr194902699 4902873 ARRDC5 chr12 11506164 11506863 PRB1 chr1 1288717512887687 PRAMEF11 chr3 10452304 10452486 ATP2B2 chr21 41450619 41450888DSCAM chr11 102738631 102738805 MMP12 chr6 55147022 55147215 HCRTR2 chr753103402 53104244 POM121L12 chr16 9857007 9858801 GRIN2A chr7 8254401882546173 PCLO chrX 140993242 140996574 MAGEC1 chr2 228881125 228884868SPHKAP chr1 190067150 190068214 FAM5C chr1 12907287 12908052 LOC649330chr22 26422410 26423627 MYO18B chr8 73848206 73850274 KCNB2 chr2142080411 42080679 DSCAM chr5 35876083 35876528 IL7R chr16 2614702626147562 HS3ST4 chr2 137813991 137814765 THSD7B chr1 13183059 13183834LOC440563 chr12 11545876 11546912 PRB2 chr6 49753679 49754855 PGK2 chr7150174218 150174831 GIMAP8 chr8 57228599 57228862 SDR16C5 chr2 229890370229890777 PID1 chr12 18891225 18892057 CAPZA3 chr10 37430660 37431196ANKRD30A chr7 141672503 141673475 TAS2R38 chr1 75036822 75039123C1orf173 chr5 145393330 145393609 SH3RF2 chr5 13753349 13753600 DNAH5chr2 107459496 107460403 ST6GAL2 chr6 54804555 54806798 FAM83B chr1956538529 56539860 NLRP5 chr12 46230544 46230718 ARID2 chr2 103274162103274336 SLC9A2 chr1 196928037 196928211 CFHR2 chr21 10916335 10916509TPTE chr7 81381415 81381589 HGF chr9 121929379 121930447 DBC1 chr513762830 13763006 DNAH5 chr20 5282767 5283371 PROKR2 chr2 226446675226447685 NYAP2 chr1 247587230 247588834 NLRP3 chr2 196749356 196749534DNAH7 chr8 57353846 57354407 PENK chr16 24372710 24373179 CACNG3 chr1143767490 143767838 PPIAL4G chr13 19748019 19748261 TUBA3C chr1240370098 240371828 FMN2 chr11 40135943 40137832 LRRC4C chr7 150324821150325587 GIMAP6 chr11 18955375 18956329 MRGPRX1 chr1 152127304152129413 RPTN chr5 13793640 13793833 DNAH5 chr19 22362771 22364371ZNF676 chr1 197390129 197391069 CRB1 chr1 117311112 117311314 CD2 chr2192700686 192701441 SDPR chr3 122002547 122004030 CASR chr2 3096631230966486 CAPN13 chr3 139297716 139297890 NMNAT3 chr17 10401044 10401218MYH1 chr8 3216703 3216877 CSMD1 chr13 103698459 103698633 SLC10A2 chr2103300624 103300798 SLC9A2 chr5 41181486 41181660 C6 chr17 75781457578319 TP53 chr8 55534675 55534849 RP1 chr12 11420521 11421056 PRB3chr8 73479975 73480514 KCNB2 chr1 233807016 233807256 KCNK1 chr4188924021 188924868 ZFP42 chr7 143175136 143175886 TAS2R41 chr5 1377657813776798 DNAH5 chr7 136699706 136700966 CHRM2 chr10 50315670 50315893VSTM4 chr5 156381434 156381696 TIMD4 chr5 140558037 140559873 PCDHB8chr8 139163459 139165440 FAM135B chr2 108487224 108489211 RGPD4 chr1197396609 197397108 CRB1 chr8 52320675 52322010 PXDNL chr5 4526203245262839 HCN1 chr3 96706247 96706814 EPHA6 chr3 121980431 121981249 CASRchr19 31038957 31040239 ZNF536 chr7 150217094 150217919 GIMAP7 chr1470633362 70635118 SLC8A3 chr7 86394511 86394878 GRM3 chr5 3506537235066067 PRLR chr1 157514068 157514311 FCRL5 chr14 94756231 94756929SERPINA10 chr21 41719668 41719842 DSCAM chr2 209113025 209113199 IDH1chr6 55638856 55639030 BMP5 chr7 6426791 6426965 RAC1 chr12 76352117635385 CD163 chr7 117175296 117175470 CFTR chr4 158057627 158057801GLRB chr19 43762386 43762596 PSG9 chr17 10399578 10399790 MYH1 chr209546581 9547020 PAK7 chr3 54958663 54959241 LRTM1 chrX 151869542151870061 MAGEA6 chrX 105449517 105451061 MUM1L1 chr9 104432377104433303 GRIN3A chrX 139865857 139866502 CDR1 chr11 129306708 129306904BARX2 chr19 56423088 56424654 NLRP13 chr2 230910665 230911384 SLC16A14chrX 141290652 141291767 MAGEC2 chr10 27702222 27703028 PTCHD3 chr3168833183 168834491 MECOM chr16 19451377 19452048 TMC5 chr6 128134092128135061 THEMIS chr12 125834042 125834786 TMEM132B chr7 150269243150270062 GIMAP4 chr7 100349366 100350706 ZAN chr6 63990056 63991126LGSN chr12 11461251 11461805 PRB4 chr10 37507967 37508797 ANKRD30A chr1463174333 63175165 KCNH5 chr2 132021042 132021875 POTEE chr6 2854242728543881 SCAND3 chr5 135692305 135693068 TRPC7 chr12 117768164 117768857NOS1 chr7 143140576 143141494 TAS2R60 chr20 1616835 1617043 SIRPG chr2020033039 20033213 CRNKL1 chr12 81112658 81112832 MYF5 chr19 5901078259010956 SLC27A5 chr22 16266924 16267098 POTEH chr5 38881693 38881867OSMR chr5 168233434 168233608 SLIT3 chr1 145296319 145296493 NBPF10 chr7146997220 146997394 CNTNAP2 chr6 28501717 28501891 GPX5 chr12 132547028132547202 EP400 chr21 10920036 10920210 TPTE chr3 7188164 7188338 GRM7chr1 16892122 16892296 NBPF1 chr5 13727603 13727777 DNAH5 chr2 228886430228886640 SPHKAP chr1 34208908 34209161 CSMD2 chr1 196952004 196952178CFHR5 chr2 185798307 185798481 ZNF804A chr1 57347134 57347308 C8A chr1620476858 20477032 ACSM2A chr4 107845185 107845359 DKK2 chr18 5255647652556650 RAB27B chr8 2813104 2813278 CSMD1 chr7 34851341 34851515 NPSR1chr22 16279160 16279334 POTEH chr2 196759765 196759939 DNAH7 chr8131921945 131922119 ADCY8 chr16 20548544 20548718 ACSM2B chr12 1869108018691254 PIK3C2G chr18 28968320 28968494 DSG4 chr19 55417848 55418022NCR1 chr18 51025713 51025887 DCC chr20 41419836 41420049 PTPRT chr3121712023 121712803 ILDR1 chr3 38888195 38889215 SCN11A chr8 105405030105405207 DPYS chr3 38770041 38770340 SCN10A chr20 40980728 40980904PTPRT chr16 70954500 70955014 HYDIN chr12 7639970 7640270 CD163 chr10124457271 124457788 C10orf120 chr6 136599029 136599819 BCLAF1 chr1938951023 38951200 RYR1 chr4 71275138 71275789 PROL1 chr4 104510866104511124 TACR3 chr17 12655754 12656628 MYOCD chr1 176525475 176526349PAPPA2 chr4 187454896 187455693 MTNR1A chr3 39307001 39307959 CX3CR1chr7 146829337 146829554 CNTNAP2 chr17 10434960 10435140 MYH2 chr10124402647 124402908 DMBT1 chr15 86807596 86808063 AGBL1 chr19 5636913456370574 NLRP4 chr3 108072295 108072560 HHLA2 chr19 43680035 43680256PSG5 chr4 9783735 9785082 DRD5 chr5 36049046 36049521 UGT3A2 chr7123593678 123594502 SPAM1 chr1 175375366 175375846 TNR chr12 3355974333560284 SYT10 chr5 41149354 41149542 C6 chr4 80327830 80329316 GK2chr12 7531616 7531889 CD163L1 chr1 159799732 159799921 SLAMF8 chr10124358298 124358572 DMBT1 chr21 41414345 41414577 DSCAM chr5 4271855842719405 GHR chr3 169539795 169540644 LRRIQ4 chr5 121786322 121787255SNCAIP chr7 150163828 150164384 GIMAP8 chr8 110456922 110457857 PKHD1L1chr5 13900317 13900510 DNAH5 chrX 151303133 151304072 MAGEA10 chr541382017 41382513 PLCXD3 chr7 154875938 154876130 HTR5A chr18 2857677028577003 DSC3 chr19 57646301 57647423 ZIM3 chr12 18234136 18234370 RERGLchr2 141773268 141773463 LRP1B chr1 152975540 152975922 SPRR3 chr513841809 13842004 DNAH5 chr6 165715218 165715663 C6orf118 chr10124380646 124380883 DMBT1 chr6 100841375 100841762 SIM1 chr20 1966576619666005 SLC24A3 chr1 152552163 152552402 LCE3D chr4 111397572 111398147ENPEP chr2 234652180 234652466 DNAJB3 chr1 57480637 57481087 DAB1 chr526881297 26881689 CDH9 chr2 125261884 125262127 CNTNAP5 chr18 5027845750278698 DCC chr1 147380099 147381357 GJA8 chr12 126138150 126139215TMEM132B chr1 159557900 159558414 APCS chr19 55107115 55107359 LILRA1chr3 96962801 96963090 EPHA6 chr1 177249565 177250636 FAM5B chr856435861 56436755 XKR4 chr12 81110909 81111199 MYF5 chr6 130761677130762861 TMEM200A chr1 248039235 248039760 TRIM58 chr19 5510656655106813 LILRA1 chr6 40399471 40400563 LRFN2 chr1 216496827 216497031USH2A chr3 7620124 7620952 GRM7 chr14 26917299 26918130 NOVA1 chr2196728871 196729703 DNAH7 chr4 100234991 100235199 ADH1B chr4 7123244271232695 SMR3A chr18 61471515 61471767 SERPINB7 chr3 38627256 38627508SCN5A chr7 150439300 150440141 GIMAP1-GIMAP5 chr19 43575869 43576077PSG2 chr6 96651050 96652078 FUT9 chr5 49699025 49699235 EMB chr338768108 38768523 SCN10A chr7 126173041 126173892 GRM8 chr3 161214629161214934 OTOL1 chr18 59483146 59483694 RNF152 chr4 70146237 70146931UGT2B28 chr21 39086552 39087409 KCNJ6 chr6 139094793 139094967 CCDC28Achr3 2928737 2928911 CNTN4 chr8 69434032 69434206 C8orf34 chr1 179631229179631403 TDRD5 chr7 34192714 34192888 BMPER chr8 110509134 110509308PKHD1L1 chr1 145281429 145281603 NOTCH2NL chr1 74492487 74492661 LRRIQ3chr20 57828025 57828199 ZNF831 chr7 146471330 146471504 CNTNAP2 chr7147092699 147092873 CNTNAP2 chr1 165218684 165218858 LMX1A chr8108334119 108334293 ANGPT1 chr5 13871663 13871837 DNAH5 chr5 1393119813931372 DNAH5 chr6 117113320 117114370 GPRC6A chr7 31918614 31918788PDE1C chr13 20048047 20048221 TPTE2 chr2 119738946 119739120 MARCO chr470156363 70156537 UGT2B28 chr12 8687232 8687406 CLEC4E chr15 3508333335083507 ACTC1 chr17 10408460 10408634 MYH1 chr8 25708106 25708280 EBF2chr7 142650881 142651055 KEL chr20 40789992 40790166 PTPRT chr2040944382 40944556 PTPRT chr1 12939546 12939720 PRAMEF4 chr3 108682264108682438 MORC1 chr2 196651727 196651901 DNAH7 chr1 196918572 196918746CFHR2 chr5 45645489 45645663 HCN1 chr2 219293987 219294161 VIL1 chr2110914315 10914489 TPTE chr8 62289153 62289327 CLVS1 chr5 1376957913769753 DNAH5 chr3 38938386 38938702 SCN11A chr8 62212397 62212832CLVS1 chr8 55533562 55534135 RP1 chr12 73012657 73012831 TRHDE chr1828725569 28725743 DSC1 chr7 141722066 141722240 MGAM chr8 118159207118159389 SLC30A8 chr16 77398090 77398273 ADAMTS18 chr1 152784961152785194 LCE1B chr11 58601913 58602311 GLYATL2 chr5 89924432 89924622GPR98 chr7 70885917 70886091 WBSCR17 chr17 10351211 10351429 MYH4 chr8110099743 110100525 TRHR chr4 70455135 70455330 UGT2A1 chr5 160721114160721433 GABRB2 chr3 130095149 130095628 COL6A5 chr7 86415599 86416389GRM3 chr5 121758578 121759419 SNCAIP chr12 2705017 2705191 CACNA1C chr3108475881 108476055 RETNLB chr2 128341744 128341918 MYO7B chr16 3153984731540021 AHSP chr3 38591831 38593038 SCN5A chr4 20620396 20620618 SLIT2chr12 118198892 118199317 KSR2 chr6 41165870 41166047 TREML2 chr1943579535 43579758 PSG2 chr12 33579074 33579406 SYT10 chr19 4323332843233512 PSG3 chr3 167023493 167023698 ZBBX chr6 25726519 25726750HIST1H2AA chr4 115997240 115998160 NDST4 chr3 38622517 38622852 SCN5Achr1 47610224 47610404 CYP4A22 chr3 189526071 189526277 TP63 chr1677401346 77401602 ADAMTS18 chr12 70946573 70946801 PTPRB chr1 1283508112835291 PRAMEF12 chr5 31322960 31323361 CDH6 chr10 28409122 28409305MPP7 chr18 61390288 61390630 SERPINB11 chr7 30795098 30795311 INMT chr526915794 26916005 CDH9 chr12 126128625 126128810 TMEM132B chr5 1373737213737605 DNAH5 chr6 55216050 55216369 GFRAL chr20 57828961 57829780ZNF831 chr12 70954498 70954695 PTPRB chr1 82408711 82409445 LPHN2 chr4138442193 138442744 PCDH18 chr6 73904254 73905119 KCNQ5 chr12 5542024555421211 NEUROD4 chr1 171251204 171251420 FMO1 chr7 37780104 37780795GPR141 chr14 95029830 95030429 SERPINA4 chrX 142795147 142795594 SPANXN2chr1 152748888 152749156 LCE1F chr5 13901375 13901592 DNAH5 chr1028023390 28023716 MKX chrX 151899863 151900744 MAGEA12 chr5 121739436121739610 SNCAIP chr2 227945124 227945298 COL4A4 chr4 70359397 70359571UGT2B4 chr10 28225644 28225818 ARMC4 chr12 79679586 79679760 SYT1 chr1710300137 10300311 MYH8 chr17 10362565 10362739 MYH4 chr8 106810955106811129 ZFPM2 chr9 127911991 127912165 PPP6C chr5 13824287 13824461DNAH5 chr5 156589595 156590571 FAM71B chr12 71029485 71029731 PTPRB chr157257765 57258456 C1orf168 chr1 158261892 158262111 CD1C chr14 9225150792251699 TC2N chr9 113703755 113704413 LPAR1 chr1 157494193 157494367FCRL5 chr3 38798166 38798340 SCN10A chr5 40964821 40964995 C7 chr2141465637 41465811 DSCAM chr11 63173954 63174128 SLC22A9 chr11 100141811100141985 CNTN5 chr1 75078336 75078510 C1orf173 chr2 183104838 183105012PDE1A chr12 100813657 100813831 SLC17A8 chr8 87738734 87738908 CNGB3chr5 41153927 41154101 C6 chr17 10432901 10433075 MYH2 chr11 113286121113286295 DRD2 chr4 166924534 166924708 TLL1 chr5 13830127 13830301DNAH5 chr7 98254233 98254458 NPTX2 chr22 26688485 26689101 SEZ6L chr161270071 1270919 CACNA1H chr2 196837004 196837182 DNAH7 chrX 151935229151935936 MAGEA3 chr21 15561359 15561697 LIPI chr10 105048126 105048323INA chr16 10273881 10274211 GRIN2A chr4 42964912 42965115 GRXCR1 chr127651533 7651782 CD163 chr4 189012596 189013034 TRIML2 chr10 5210329552103773 SGMS1 chr7 63726289 63727145 ZNF679 chr5 82948396 82948601HAPLN1 chr7 57187662 57188801 ZNF479 chr12 7867798 7868019 DPPA3 chr1096612489 96612671 CYP2C19 chr4 55139713 55139895 PDGFRA chrX 3597411835974300 CXorf22 chr1 12942929 12943184 PRAMEF4 chr14 62462738 62463261SYT16 chr10 24762204 24762897 KIAA1217 chr1 157516796 157516970 FCRL5chr8 25718564 25718738 EBF2 chr4 94137888 94138062 GRID2 chr12 2103236721032541 SLCO1B3 chrX 130218213 130218387 ARHGAP36 chr12 344235 344409SLC6A13 chr5 13717450 13717624 DNAH5 chr3 189455505 189455679 TP63 chr2155711256 155711815 KCNJ3 chrX 35993820 35994003 CXorf22 chr1 1285410512854554 PRAMEF1 chr6 55223696 55223929 GFRAL chr2 51254666 51255363NRXN1 chr21 41384997 41385255 DSCAM chr12 10783683 10783894 STYK1 chr440439811 40440698 RBM47 chr6 70070762 70071333 BAI3 chr3 3893608338936404 SCN11A chr16 20043063 20043913 GPR139 chr1 201046066 201046245CACNA1S chr19 51729080 51729289 CD33 chr4 42895294 42895591 GRXCR1 chr444176893 44177191 KCTD8 chr19 52034552 52034742 SIGLEC6 chr19 5651510356515436 NLRP5 chr8 53084354 53085076 ST18 chr1 18691757 18692086 IGSF21chr7 120385851 120386064 KCND2 chrX 105280470 105280898 SERPINA7 chr536039596 36039789 UGT3A2 chr1 75055374 75055761 C1orf173 chr14 8872955188729797 KCNK10 chr4 69433497 69434190 UGT2B17 chr16 71570728 71571674CHST4 chr4 70504801 70505137 UGT2A1 chr1 22973755 22974269 C1QC chr2031607383 31607557 BPIFB2 chr7 141635594 141635768 CLEC5A chr8 3960398339604157 ADAM2 chr4 73012772 73013480 NPFFR2 chr7 141731449 141731623MGAM chr7 141754543 141754717 MGAM chr9 78848356 78848530 PCSK5 chr1028420467 28420641 MPP7 chr12 70988298 70988472 PTPRB chr8 2432430624324480 ADAM7 chr19 50169028 50169202 BCL2L12 chr5 35957306 35957480UGT3A1 chr4 46060483 46060657 GABRG1 chr9 21974618 21974792 CDKN2A chr209417636 9417810 PLCB4 chr6 100395680 100395854 MCHR2 chr1 153122394153122568 SPRR2G chr16 70926260 70926434 HYDIN chr9 39171364 39171538CNTNAP3 chr14 20019836 20020060 POTEM chrX 65486280 65486506 HEPH chr1955106128 55106349 LILRA1 chr19 51728497 51728841 CD33 chr2 102626046102626247 IL1R2 chr3 107096456 107097221 CCDC54 chr9 21216783 21217278IFNA16 chr1 78958514 78959151 PTGFR chr10 95790860 95791925 PLCE1 chr535909981 35910155 CAPSL chr18 57103207 57103381 CCBE1 chr1 181726066181726240 CACNA1E chr19 55377963 55378137 KIR3DL2 chr12 4382610143826275 ADAMTS20 chr9 78547259 78547433 PCSK5 chr11 100169925 100170099CNTN5 chr18 31538245 31538419 NOL4 chr1 158585010 158585184 SPTA1 chr2155566125 155566299 KCNJ3 chr13 72063117 72063291 DACH1 chr10 2837859728378771 MPP7 chr5 100147666 100147840 ST8SIA4 chr12 81205307 81205481LIN7A chr5 41203173 41203347 C6 chr19 17088176 17088350 CPAMD8 chr1917091323 17091497 CPAMD8 chr6 100390822 100390996 MCHR2 chr6 117609734117609908 ROS1 chr6 70064085 70064259 BAI3 chr15 88423492 88423666 NTRK3chr4 55956096 55956270 KDR chr1 47515653 47515827 CYP4X1 chr18 5502730355027477 ST8SIA3 chr3 189587066 189587240 TP63 chr1 181767431 181767894CACNA1E chr1 192335064 192335245 RGS21 chr11 123753849 123754053 TMEM225chr4 70360874 70361511 UGT2B4 chr14 96706805 96707830 BDKRB2 chr442403051 42403226 SHISA3 chr3 46399236 46399940 CCR2 chr5 153190583153190778 GRIA1 chr10 30336467 30336728 KIAA1462 chr1 38227124 38227754EPHA10 chr3 169099048 169099284 MECOM chr12 81101507 81101934 MYF6 chr895680195 95680372 ESRP1 chr9 121976230 121976407 DBC1 chr3 3873884638739954 SCN10A chrX 140984914 140985121 MAGEC3 chr1 159273742 159273950FCER1A chr14 88477275 88478075 GPR65 chr8 39872788 39873122 IDO2 chr1240114613 40114948 C12orf40 chr5 156479422 156479659 HAVCR1 chr2217288657 17288962 XKR3 chr10 27687286 27688145 PTCHD3 chr8 8888504188886170 DCAF4L2 chr5 156816239 156816423 CYFIP2 chr11 62996843 62997107SLC22A25 chr5 151784008 151784668 NMUR2 chr5 23522741 23522957 PRDM9chr1 158224895 158225111 CD1A chr16 82032726 82033761 SDR42E1 chr1012940434 12940627 CCDC3 chr1 75072302 75072545 C1orf173 chr1 177001591177001975 ASTN1 chr17 72469698 72469918 CD300A

TABLE 19 Sup- Con- port- firm- ing Total Per- ed reads depth cent byVari- Vari- Tu- Resi- (non- (non- mu- clini- ant ant mor Ref. dueProtein de- de- tant cal Case class type Chr Position allele allele GeneRefSeq change position duped) duped) allele assay P1  Indel frame chr 177578474 +G   C TP53 NM_000546.5 none NA 41 332 12% shift P1  Indel framechr 17 29552244 −A   G NF1 NM_000267.3 none NA 117 1010 12% shift P1 Indel frame chr 17 29553484 +T   C NF1 NM_000267.3 none NA 88 643 14%shift P1  Indel intron chr 17 29592185 −T   C NF1 NM_000267.3 none NA127 936 14% P1  SNV utr-5 chr 1  156785560 A G NTRK1, NM_001007792.1none NA 40 738  5% SH2D2A P1  SNV intron chr 1  157806043 T G CD5LNM_005894.2 none NA 44 319 14% P1  SNV coding- chr 1  248525206 G COR2T4 NM_001004696.1 none 108/349 47 552  9% synony- mous P1  SNV intronchr 2  33500291 C T LTBP1 NM_000627.3 none NA 48 238 20% P1  SNV mis-chr 4  55946307 A C KDR NM_002253.2 ARG > 1291/1357 264 1001 26% senseMET P1  SNV intron chr 4  55963949 G A KDR NM_002253.2 none NA 202 96021% P1  SNV mis- chr 4  55968672 A C KDR NM_002253.2 ARG >  664/1357 162982 17% sense LEU P1  SNV intron chr 6  117642146 C T ROS1 NM_002944.2none NA 305 1397 22% P1  SNV mis- chr 9  8376700 T G PTPRD NM_002839.3SER > 1471/1913 339 1196 28% sense ARG P1  SNV intron chr 9  8733625 T CPTPRD NM_001040712.2 none NA 85 265 32% P1  SNV intron chr 10 43611663 TG RET NM_020630.4 none NA 54 588  9% P1  SNV utr-3 chr 15 88522525 T GNTRK3 NM_001007156.2 none NA 67 724  9% P2  Indel intron chr 2  79314100+A   C REG1B NM_006507.3 none NA 981 4086 24% P2  SNV splice- chr 2 50463926 A C NRXN1 NM_001135659.1 none NA 2904 8529 34% 5 P2  SNV intronchr 3  89457148 G A EPHA3 NM_005233.5 none NA 2668 4414 60% P2  SNVintron chr 3  89468286 T G EPHA3 NM_005233.5 none NA 838 4066 21% P2 SNV intron chr 3  89480240 T A EPHA3 NM_005233.5 none NA 786 3722 21%P2  SNV utr-3 chr 4  66189669 T A EPHA5 NM_004439.5 none NA 575 1632 35%P2  SNV intron chr 4  66242868 T G EPHA5 NM_004439.5 none NA 1849 284965% P2  SNV intron chr 5  176522747 A C FGFR4 NM_002011.3 none NA 19382637 73% P2  SNV intron chr 6  117648229 C T ROS1 NM_002944.2 none NA3047 8531 36% P2  SNV mis- chr 12 78400637 A C NAV3 NM_014903.4 PRO > 440/2364 1414 8119 17% sense HIS P2  SNV mis- chr 12 78400910 T G NAV3NM_014903.4 GLY >  531/2364 3069 8571 36% sense VAL P2  SNV mis- chr 177577551 T C TP53 NM_000546.5 GLY > 244/394 3294 4966 66% sense SER P2 SNV intron chr 19 1207247 T G STK11 NM_000455.4 none NA 1067 2876 37%P3  SNV mis- chr 17 7578253 A C TP53 NM_000546.5 GLY > 199/394 455 440910% sense VAL P4  SNV mis- chr 2  212248555 T C ERBB4 NM_005235.2 ASP >1238/1309 1006 4095 25% sense ASN P4  SNV mis- chr 12 25398281 T C KRASNM_033360.2 GLY >  13/190 1196 4536 26% yes sense ASP P5  SNV mis- chr7  55249071 T C EGFR NM_005228.3 THR >  790/1211 659 7660  9% yes senseMET P5  SNV mis- chr 7  55259515 G T EGFR NM_005228.3 LEU >  858/12114170 11863 35% yes sense ARG P5  SNV near- chr 11 55135338 A G none nonenone NA 716 3285 22% gene- 5 P5  SNV mis- chr 17 7577097 T C TP53NM_000546.5 ASP > 281/394 2539 5928 43% sense ASN P6  SNV coding- chr 1278400791 A G NAV3 NM_014903.4 none  491/2364 1223 2615 47% synony- mousP6  SNV mis- chr 12 129822187 T G TMEM132D NM_133448.2 LEU >  431/11001595 2989 53% sense MET P6  SNV stop- chr 17 7578275 A G TP53NM_000546.5 GLN > 192/394 3795 3825 99% gained stop P6  SNV coding- chr9  8500803 A G PTPRD NM_002839.3 none NA 643 8021  8% synony- mous P11SNV intron chr 2  29448209 T C ALK none none NA 2011 8410 24% P11 SNVmis- chr 21 44524456 A G U2AF1 NM_006758.2 SER >  34/241 1607 7775 21%sense PHE P12 Indel frame chr 17 7577057 −C   T TP53 NM_000546.5 none NA597 2735 22% shift P12 SNV intron chr 4  55973786 T C KDR NM_002253.2none NA 349 1439 24% P12 SNV intron chr 6  117650296 T G ROS1NM_002944.2 none NA 889 4857 18% P12 SNV mis- chr 7  41729291 G T INHBANM_002192.2 LYS > 413/427 186 3516  5% sense THR P12 SNV intron chr 9 8471102 T A PTPRD NM_001040712.2 none NA 747 3019 25% P12 SNV mis- chr12 25380276 G T KRAS NM_033360.2 GLN >  61/190 321 4104  8% sense PROP12 SNV mis- chr 19 10602473 A C KEAP1 NM_012289.3 VAL > 369/625 6192783 22% sense LEU P13 SNV mis- chr 1  190067540 T C FAM5C NM_199051.1GLY > 637/767 404 2983 14% sense SER P13 SNV stop- chr 5  45461969 T CHCN1 NM_021072.3 TRP > 330/891 341 4749  7% gained stop P13 SNV intronchr 8  38276015 G C FGFR1 NM_001174063.1 none NA 543 4016 14% P13 SNVmis- chr 15 88483904 T C NTRK3 NM_001012338.2 GLU > 556/840 839 4713 18%sense LYS P13 SNV mis- chr 17 7577538 T C TP53 NM_000546.5 ARG > 248/394269 2190 12% sense GLN P14 SNV mis- chr 1  156841521 C A NTRK1NM_002529.3 GLU > 275/797 710 1583 45% sense ALA P14 SNV intron chr 3 89176334 T G EPHA3 NM_005233.5 none NA 969 1873 52% P14 SNV coding- chr7  55249159 A G EGFR NM_005228.3 none  819/1211 796 1509 53% synony-mous P14 SNV mis- chr 7  55259515 G T EGFR NM_005228.3 LEU >  858/1211251 2044 12% yes sense ARG P14 SNV intron chr 10 43607789 T C RETNM_020630.4 none NA 688 1544 45% P14 SNV mis- chr 17 7577545 C T TP53NM_000546.5 MET > 246/394 213 1452 15% sense VAL P14 SNV mis- chr 1729553484 T C NF1 NM_001042492.2 PRO >  678/2840 590 1192 50% sense LEUP14 SNV mis- chr 19 1223125 G C STK11 NM_000455.4 PHE > 354/434 968 165958% sense LEU P15 Indel intron chr 17 29533514 +T   G NF1 NM_000267.3none NA 161 1109 15% P15 SNV mis- chr 1  70226008 T G LRRC7 NM_020794.2VAL >   41/1538 653 6399 10% sense PHE P15 SNV missense chr 1  144882833A C PDE4DIP NM_001198834.2 GLN > 1062/2363 457 8590  5% HIS P15 SNV mis-chr 1  190203515 A C FAM5C NM_199051.1 LYS > 237/767 210 3488  6% senseASN P15 SNV mis- chr 1  248525334 A C OR2T4 NM_001004696.1 ALA > 151/349562 3071 18% sense ASP P15 SNV intron chr 2  155157911 A C GALNT13NM_052917.2 none NA 215 3469  6% P15 SNV intron chr 2  212495103 A GERBB4 NM_001042599.1 none NA 512 4067 13% P15 SNV utr-3 chr 3  89528742T G EPHA3 NM_005233.5 none NA 50 710  7% P15 SNV coding- chr 4  55979517T G KDR NM_002253.2 none  310/1357 909 4871 19% synony- mous P15 SNVutr-3 chr 4  66189751 A C EPHA5 NM_004439.5 none NA 120 2226  5% P15 SNVintron chr 4  66233002 A C EPHA5 NM_004439.5 none NA 391 1427 27% P15SNV intron chr 4  66233003 A C EPHA5 NM_004439.5 none NA 487 1523 32%P15 SNV intron chr 4  66233146 T G EPHA5 NM_004439.5 none NA 553 345916% P15 SNV mis- chr 5  176523126 A C FGFR4 NM_002011.3 ASP > 630/803860 4341 20% sense GLU P15 SNV coding- chr 5  176524647 A C FGFR4NM_002011.3 none 793/803 203 3896  5% synony- mous P15 SNV mis- chr 7 41729339 A C INHBA NM_002192.2 ARG > 397/427 735 4383 17% sense ILE P15SNV intron chr 8  87738607 A C CNGB3 NM_019098.4 none NA 199 1839 11%P15 SNV intron chr 8  113563115 A C CSMD3 NM_052900.2 none NA 415 410810% P15 SNV mis- chr 9  8528716 A C PTPRD NM_002839.3 ARG >  139/1913735 3641 20% sense LEU P15 SNV mis- chr 9  138439735 A T OBP2ANM_014582.2 ILE >  99/171 783 3487 22% sense LYS P15 SNV intron chr 1043608292 A C RET NM_020630.4 none NA 401 3402 12% P15 SNV intron chr 1043608755 T C RET NM_020630.4 none NA 408 4206 10% P15 SNV mis- chr 1155135855 A C OR4A15 NM_001005275.1 ARG > 166/345 1143 4667 24% sense SERP15 SNV mis- chr 12 25398284 T C KRAS NM_033360.2 GLY >  12/190 254 4577 6% yes sense ASP P15 SNV mis- chr 13 48954333 T C RB1 NM_000321.2 SER >485/929 251 4856  5% sense PHE P15 SNV intron chr 13 48954451 T G RB1NM_000321.2 none NA 222 2178 10% P16 Indel intron chr 2  212295977 +T  A ERBB4 NM_001042599.1 none NA 160 1138 14% P16 Indel frame chr 191220638 −C   T STK11 NM_000455.4 none NA 279 3306  8% shift P16 SNVcoding- chr 1  156843429 A G NTRK1 NM_002529.3 none 285/797 106 2064  5%synony- mous P16 SNV coding- chr 1  181708291 T C CACNA1E NM_001205293.1none 1207/2314 252 4341  6% synony- mous P16 SNV coding- chr 1 248525326 A C OR2T4 NM_001004696.1 none 148/349 208 4051  5% synony-mous P16 SNV intron chr 2  125530343 A C CNTNAP5 NM_130773.2 none NA 3124546  7% P16 SNV coding- chr 2  212530083 A C ERBB4 NM_005235.2 none 612/1309 322 5104  6% synony- mous P16 SNV coding- chr 2  212587119 C TERBB4 NM_005235.2 none 294/1309 442 4704  9% synony- mous- near- spliceP16 SNV intron chr 4  55958900 T G KDR NM_002253.2 none NA 304 4371  7%P16 SNV intron chr 4  55962358 C T KDR NM_002253.2 none NA 530 3346 16%P16 SNV mis- chr 4  55968588 A C KDR NM_002253.2 GLY >  692/1357 3005352  6% sense VAL P16 SNV coding- chr 4  55970963 G A KDR NM_002253.2none  612/1357 495 5149 10% synony- mous P16 SNV intron chr 4  55971241A C KDR NM_002253.2 none NA 231 2622  9% P16 SNV intron chr 5  19473838T G CDH18 NM_001167667.1 none NA 225 3964  6% P16 SNV mis- chr 5 112176654 A G APC NM_000038.5 ARG > 1788/2844 395 7777  5% sense HIS P16SNV intron chr 5  176520134 T G FGFR4 NM_002011.3 none NA 167 3238  5%P16 SNV intron chr 7  11501543 T G THSD7A NM_015204.2 none NA 97 1694 6% P16 SNV utr-5 chr 7  53103357 A C POM121L12 NM_182595.3 none NA 631228  5% P16 SNV mis- chr 7  116411990 T C MET NM_001127500.1 THR >1010/1409 831 7410 11% sense ILE P16 SNV intron chr 10 43606641 A C RETNM_020630.4 none NA 201 2822  7% P16 SNV intron chr 11 534195 A G HRASNM_001130442.1 none NA 252 2619 10% P16 SNV mis- chr 11 108143456 G CATM NM_000051.3 PRO > 1054/3057 744 6374 12% sense ARG P16 SNV mis- chr12 25398284 A C KRAS NM_033360.2 GLY >  12/190 942 7146 13% yes senseVAL P16 SNV coding- chr 13 48947619 A C RB1 NM_000321.2 none 402/929 4007741  5% synony- mous P16 SNV intron chr 13 70314492 T C KLHL1NM_020866.2 none NA 524 4290 12% P16 SNV intron chr 13 70314809 A TKLHL1 NM_020866.2 none NA 471 2018 23% P16 SNV intron chr 15 88472337 CG NTRK3 NM_001012338.2 none NA 149 2747  5% P16 SNV intron chr 177578132 A C TP53 NM_000546.5 none NA 76 1470  5% P17 SNV mis- chr 7 81386606 T G HGF NM_000601.4 ASN > 127/729 276 4991  6% sense LYS P17SNV mis- chr 12 25398285 A C KRAS NM_033360.2 GLY >  12/190 437 4384 10%yes sense CYS

TABLE 20 Non-deduped Sample description/ % patient (P#)/ No. of properlySelector Median Sample healthy control reads % reads paired No. of readson-target Median fragment count (C#) mapped mapped reads on-target ratedepth length 1 H3122 0.1% into 24503042 99.0% 96.8% 17041857 69.5% 8688173 HCC78 2 H3122 1% into 19199810 98.9% 96.7% 13173049 69.8% 8657 171HCC78 3 H3122 10% into 19329153 98.9% 96.5% 13486460 69.8% 6890 170HCC78 4 H3122 100% 24470094 99.0% 96.8% 16789007 68.6% 6739 174 5 HCC78100% 21276865 99.0% 96.9% 14835137 69.7% 7602 172 6 HCC78 10% into9023859 97.5% 83.3% 5351003 59.3% 2682 170 C1 plasma DNA 4 cycles 7HCC78 10% into 7852585 79.5% 72.0% 3958384 50.4% 15 158 C1 plasma DNAS 8cycles SigmaWGA 8 HCC78 10% into 26605244 97.7% 87.2% 16066902 60.4%8261 169 C1 plasma DNA 6 cycles 9 HCC78 10% into 19811700 96.9% 91.8%12098869 61.1% 6258 166 C1 plasma DNA 8 cycles NEBNextOvernightBead 10HCC78 10% into 30672877 98.0% 93.1% 18671777 60.9% 9862 167 C1 plasmaDNA 8 cycles OrigNEBNext 15 minLig 11 HCC78 10% into 37509063 97.6%87.6% 22690732 60.5% 11630 169 C1 plasma DNA 4 ng 9 cycles 12 HCC780.025% 17409235 98.2% 87.0% 8055464 46.3% 3913 169 into C1 plasma DNA 13HCC78 0.05% 30253156 98.1% 86.1% 13529312 44.7% 6549 169 into C1 plasmaDNA 14 HCC78 0.1% 31335854 98.4% 88.1% 14071945 44.9% 6897 169 into C1plasma DNA 15 HCC78 0.5% 35236429 98.8% 89.8% 16277998 46.2% 8096 169into C1 plasma DNA 16 HCC78 1% 33272947 98.5% 89.8% 15528745 46.5% 7779171 into C1 plasma DNA 17 P1  21702598 99.3% 97.1% 12400852 57.1% 7336220 18 P2  22430498 99.2% 97.5% 12942388 57.7% 7680 235 19 P3  2596143199.3% 97.8% 14809108 57.0% 8838 235 20 P4  21912624 99.1% 96.5% 1238926856.5% 7331 227 21 P5  23357455 99.2% 97.2% 13712765 58.7% 8155 219 22P6  11356360 96.7% 92.6% 7626499 67.2% 3848 152 23 P7  10342837 97.1%93.5% 6943003 67.1% 3552 155 24 P8  11888370 96.9% 93.0% 7827674 65.8%4021 154 25 P9  17626969 97.0% 94.4% 10437704 59.2% 5441 172 26 P1013290607 96.9% 93.6% 8680450 65.3% 4572 161 27 P11 22496393 96.7% 93.8%13270664 59.0% 6970 169 28 P12 21230200 98.8% 97.7% 8703464 40.5% 4710258 29 P13 24801066 97.8% 96.6% 9933117 39.2% 5324 252 30 P14 2187376497.7% 96.4% 9032079 40.3% 4867 248 31 P15 23130748 97.9% 96.8% 934315339.6% 5041 253 32 P16 22245944 98.1% 97.2% 8955379 39.5% 4816 263 33 P1725906115 97.9% 97.2% 10775948 40.7% 5816 239 34 P1  2916102 94.6% 90.1%1776887 60.9% 976 192 35 P2  21639699 99.0% 97.1% 13491073 62.3% 7247204 36 P3  23518792 99.3% 98.0% 15524732 66.0% 9562 204 37 P4  1195939997.5% 94.1% 7178723 60.0% 3968 189 38 P5  20192824 98.8% 97.0% 1283204063.5% 6930 187 39 P6  7773013 87.0% 81.8% 5027345 64.7% 2445 158 40 P7 14127683 94.1% 89.3% 9045653 64.0% 4793 162 41 P8  16093442 91.7% 85.4%10242535 63.6% 5331 151 42 P9  24980306 99.2% 97.3% 13824322 55.3% 7312239 43 P10 15408447 94.0% 89.6% 10038486 65.1% 5335 157 44 P11 2338221293.4% 88.3% 14342719 61.3% 7700 156 45 P12 17316416 96.7% 95.9% 730456140.8% 3836 230 46 P13 15170651 97.7% 97.4% 6292372 40.5% 3308 241 47 P147141267 95.1% 96.2% 3096168 41.2% 1650 187 48 P15 19706548 97.6% 97.4%8720851 43.2% 4538 209 49 P16 19889232 98.0% 98.3% 9011417 44.4% 4734220 50 P17 18092543 98.5% 97.7% 7781779 42.4% 4280 238 51 C1 2676622497.5% 86.7% 16147472 60.3% 8280 168 52 C2 20092668 98.2% 90.2% 991665348.5% 5089 176 53 C3 16454970 97.4% 89.2% 8206791 48.6% 4199 175 54 C422388109 97.3% 88.0% 11165306 48.5% 5562 175 55 C5 21899643 97.6% 86.4%11005231 49.1% 5525 170 56 P1 time point 1 14656874 99.0% 85.0% 947501564.6% 5079 171 57 P1 time point 2 18861849 99.4% 84.7% 12093175 64.1%6487 172 58 P1 time point 3 23920634 97.5% 84.7% 11695968 47.7% 5768 17359 P2 time point 1 18474671 99.4% 86.9% 12436916 67.3% 6876 172 60 P2time point 2 13894587 99.5% 96.4% 8839565 63.6% 5248 185 61 P2 timepoint 3 20191825 97.5% 96.5% 9874542 47.7% 5370 182 62 P3 time point 120880669 99.2% 86.0% 13261172 63.5% 7057 170 63 P3 time point 2 2963169799.3% 86.5% 18805559 63.5% 10089 171 64 P4 time point 1 19128070 99.0%87.4% 12679761 66.3% 6971 169 65 P4 time point 2 27673936 99.4% 85.9%18257927 66.0% 9926 171 66 P5 time point 1 19610825 99.3% 87.8% 1306949266.6% 7604 169 68 P5 time point 2 23075293 98.0% 93.9% 11383523 48.3%6105 176 67 P5 time point 3 28075947 99.4% 88.0% 18938907 67.5% 10451170 69 P6 time point 1 47768468 98.6% 91.3% 22179023 46.4% 11172 166 70P6 time point 2 35775847 98.5% 92.0% 16677920 46.6% 8455 166 71 P9 timepoint 1 19595585 99.1% 84.2% 12848481 65.6% 6839 172 72 P9 time point 218474032 98.4% 83.9% 12047199 65.2% 6043 169 73 P9 time point 3 2199627299.4% 88.7% 14859835 67.6% 8141 167 74 P9 time point 4 24577249 98.0%90.4% 12087359 48.2% 6256 174 75 P9 time point 5 22592773 97.6% 84.1%11325418 48.9% 5572 170 76 P12 time point 1 11793847 99.1% 89.1% 761226164.0% 3946 168 77 P12 time point 2 18761346 98.6% 85.2% 9483960 49.8%4704 172 78 P13 time point 1 15097466 98.1% 88.4% 9550125 62.1% 4921 16779 P13 time point 2 20074378 98.3% 86.7% 12405223 60.8% 6283 171 80 P14time point 1 20510385 98.2% 87.8% 12803787 61.3% 6483 168 81 P14 timepoint 2 20676149 97.5% 87.5% 10489917 49.5% 5275 167 82 P15 time point 116113392 97.8% 84.3% 9826356 59.7% 4802 171 83 P15 time point 2 1761189698.5% 96.7% 10299562 57.6% 5638 184 84 P15 time point 3 21463621 98.2%87.0% 13024286 59.6% 6534 174 85 P15 time point 4 14616334 97.6% 83.4%8751266 58.4% 4349 173 86 P15 time point 5 15582630 98.1% 86.4% 950565659.8% 4840 175 87 P16 time point 1 16329648 97.3% 85.7% 10088350 60.1%5069 173 88 P16 time point 2 25438935 98.2% 87.4% 12932279 49.9% 6587169 89 P16 time point 3 20158925 98.2% 86.5% 12591048 61.4% 6399 169 90P17 time point 1 13920942 98.5% 97.1% 8358972 59.1% 4521 183

TABLE 21 Deduped (by coordinates & sequence)^(a) Fraction EstimatedSample of Fold % possible description/ possible increase genome patient(P#)/ No. of Selector genome in library equivalents Sample healthycontrol reads Duplication on-target Median equivalents complexitysequenced count (C#) mapped rate rate depth (%)^(b) (het SNPs)^(c) (hetSNPs)^(d) 1 H3122 0.1% into 9447750 61% 60.2% 2922.5  34% 1.06  36%HCC78 2 H3122 1% into 7363376 62% 58.5% 2263  26% 1.07  28% HCC78 3H3122 10% into 8585796 56% 61.4% 2711  39% 1.06  42% HCC78 4 H3122 100%9405562 62% 60.7% 2922  43% 1.06  46% 5 HCC78 100% 8433702 60% 60.8%2649  35% 1.05  37% 6 HCC78 10% into 4864712 46% 56.1% 1364  51% 1.27 65% C1 plasma DNA 4 cycles 7 HCC78 10% into 1506958 81% 15.4% 8  53%1.07  57% C1 plasma DNA 8 cycles Sigma WGA 8 HCC78 10% into 12258172 54%51.4% 3107  38% 1.44  54% C1 plasma DNA 6 cycles 9 HCC78 10% into9160482 54% 51.6% 2414  39% 1.40  54% C1 plasma DNA 8 cyclesNEBNextOvernightBead 10 HCC78 10% into 12128078 60% 46.3% 2830  29% 1.42 41% C1 plasma DNA 8 cycles OrigNEBNext 15 minLig 11 HCC78 10% into9488082 75% 32.1% 1447 100% 1.19 100% C1 plasma DNA 4 ng 9 cycles 12HCC78 0.025% 9477184 46% 34.8% 1548  40% 1.26  50% into C1 plasma DNA 13HCC78 0.05% 15575778 49% 33.1% 2424  37% 1.37  51% into C1 plasma DNA 14HCC78 0.1% 17236094 45% 32.9% 2703  39% 1.40  55% into C1 plasma DNA 15HCC78 0.5% 18212006 48% 33.3% 2889  36% 1.41  50% into C1 plasma DNA 16HCC78 1% into 17692196 47% 33.6% 2845  37% 1.40  51% C1 plasma DNA 17P1  9849054 55% 52.1% 3018  41% 1.06  44% 18 P2  12321552 45% 55.1% 3999 52% 1.06  55% 19 P3  13958798 46% 54.1% 4489  51% 1.06  54% 20 P4 10554320 52% 51.9% 3215  44% 1.05  46% 21 P5  12655290 46% 55.9% 4205 52% 1.06  55% 22 P6  5985032 47% 63.0% 1940  50% 1.09  55% 23 P7 5330048 48% 62.5% 1729  49% 1.07  52% 24 P8  6048134 49% 61.6% 1946  48%1.08  52% 25 P9  10297340 42% 54.4% 2924  54% 1.08  58% 26 P10 662115250% 59.6% 2114  46% 1.07  49% 27 P11 12588032 44% 53.2% 3529  51% 1.08 55% 28 P12 11268046 47% 37.0% 2274  48% 1.03  50% 29 P13 12409366 50%35.9% 2433  46% 1.03  47% 30 P14 11153394 49% 37.2% 2278  47% 1.03  48%31 P15 12056584 48% 36.6% 2415  48% 1.03  50% 32 P16 12219738 45% 36.7%2451  51% 1.03  52% 33 P17 12958646 50% 37.2% 2636  45% 1.04  47% 34 P1 1409454 52% 57.1% 435  45% 1.03  46% 35 P2  9764204 55% 56.6% 2976  41%1.05  43% 36 P3  11211374 52% 62.8% 4308  45% 1.07  48% 37 P4  614926449% 56.3% 1912  48% 1.04  50% 38 P5  7456332 63% 54.0% 2095  30% 1.05 32% 39 P6  4146734 47% 60.4% 1247  51% 1.06  54% 40 P7  5946980 58%53.7% 1709  36% 1.04  37% 41 P8  6173080 62% 51.8% 1695  32% 1.05  33%42 P9  12548696 50% 50.7% 3395  46% 1.05  49% 43 P10 5951104 61% 52.1%1657  31% 1.04  32% 44 P11 10862910 54% 50.6% 2938  38% 1.07  41% 45 P127950700 54% 34.9% 1479  39% 1.03  40% 46 P13 3922778 74% 15.8% 317  21%1.03  22% 47 P14 3088542 57% 34.7% 566  34% 1.02  35% 48 P15 4519878 77%12.4% 284  20% 1.06  21% 49 P16 4361750 78% 12.1% 266   7% 1.03   7% 50P17 8267660 54% 35.4% 1594  37% 1.03  38% 51 C1 11839302 56% 50.7% 2955 36% 1.43  51% 52 C2 5816892 71% 14.9% 424  53% 1.11  59% 53 C3 828246650% 38.1% 1575  38% 1.26  47% 54 C4 6079494 73% 11.9% 341  91% 1.13 100%55 C5 9758232 55% 33.6% 1546  28% 1.28  36% 56 P1 time point 1 368048875% 52.4% 948  22% 1.34  30% 57 P1 time point 2 3733984 80% 46.8% 856 37% 1.25  46% 58 P1 time point 3 11150518 53% 35.0% 1818  32% 1.31  41%59 P2 time point 1 5340414 71% 57.8% 1608  37% 1.29  48% 60 P2 timepoint 2 4772686 66% 56.2% 1559  30% 1.21  36% 61 P2 time point 310102650 50% 37.2% 2045  38% 1.24  47% 62 P3 time point 1 6710612 68%50.8% 1702  34% 1.33  46% 63 P3 time point 2 9571240 68% 51.5% 2474  47%1.42  66% 64 P1 time point 1 5119914 73% 54.4% 1424  43% 1.27  55% 65 P4time point 2 8288640 70% 55.9% 2351  45% 1.40  62% 66 P5 time point 15185064 74% 53.4% 1527  51% 1.32  68% 68 P5 time point 2 11429884 50%37.2% 2235  37% 1.30  48% 67 P5 time point 3 7875654 72% 56.0% 2255  46%1.38  63% 69 P6 time point 1 21842910 54% 28.2% 3003  54% 1.41  76% 70P6 time point 2 18629126 48% 32.6% 3023  46% 1.44  66% 71 P9 time point1 5114308 74% 52.8% 1316  33% 1.29  43% 72 P9 time point 2 3767226 80%46.5% 791  14% 1.24  17% 73 P9 time point 3 6988880 68% 59.1% 2153  41%1.40  57% 74 P9 time point 4 12801394 48% 39.2% 2553  41% 1.34  55% 75P9 time point 5 11359054 50% 39.1% 2136  38% 1.37  53% 76 P12 time point1 4998908 58% 53.3% 1307  33% 1.25  41% 77 P12 time point 2 9297216 50%37.8% 1682  36% 1.29  46% 78 P13 time point 1 6320228 58% 52.9% 1661 34% 1.31  44% 79 P13 time point 2 6366844 68% 45.8% 1441  29% 1.28  37%80 P14 time point 1 7239082 65% 48.2% 1689  30% 1.33  39% 81 P14 timepoint 2 10120132 51% 38.5% 1898  36% 1.34  48% 82 P15 time point 15848926 64% 50.2% 1453  30% 1.33  40% 83 P15 time point 2 7756082 56%49.6% 2093  37% 1.26  47% 84 P15 time point 3 4418526 79% 31.2% 667  29%1.11  32% 85 P15 time point 4 5921542 59% 49.2% 1416  33% 1.28  42% 86P15 time point 5 4156694 73% 39.8% 813  32% 1.20  39% 87 P16 time point1 5626572 66% 46.9% 1282  25% 1.25  32% 88 P16 time point 2 9929984 61%28.3% 1336  64% 1.34  86% 89 P16 time point 3 8175762 59% 50.9% 2019 32% 1.32  42% 90 P17 time point 1 3945290 72% 40.6% 842  21% 1.18  25%^(a)Statistics for post-duplicate reads ^(b)Theoretically maximum numberof input genomic equivalents sequenced (minimum of input (Table3-Expected haploid genome copies) and depth sequenced (Table 20 -MedianDepth) ^(c)A maximum of 100% is possible. ^(d)Maximum number of inputgenomic equivalents sequenced (Fraction of possible genome equivalent) ×fold increase in library complexity. Maximum value is 100%

All patents, patent publications, and other published referencesmentioned herein are hereby incorporated by reference in theirentireties as if each had been individually and specificallyincorporated by reference herein.

While specific examples have been provided, the above description isillustrative and not restrictive. Any one or more of the features of thepreviously described embodiments can be combined in any manner with oneor more features of any other embodiments in the present invention.Furthermore, many variations of the invention will become apparent tothose skilled in the art upon review of the specification. The scope ofthe invention should, therefore, be determined by reference to theappended claims, along with their full scope of equivalents.

What is claimed is:
 1. A method of detecting, diagnosing, prognosing, ortherapy selection of a cancer in a subject in need thereof, the methodcomprising: (a) obtaining sequence information of a cell-free DNA(cfDNA) sample derived from the subject; and (b) using the sequenceinformation derived from (a) to detect circulating tumor DNA (ctDNA) inthe sample, wherein the method is capable of detecting a percentage ofctDNA that is less than or equal to 2% of total cfDNA.
 2. The method ofclaim 1, wherein the method is capable of detecting a percentage ofctDNA that is less than or equal to 1.75%, 1.5%, 1.25%, 1%, 0.75%,0.50%, 0.25%, 0.1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%,0.1%, 0.05%, 0.01%, 0.009%, 0.008%, 0.007%, 0.006%, 0.005%, 0.004%,0.003%, 0.002%, 0.001%, 0.0005%, or 0.00001% of the total cfDNA.
 3. Themethod of claim 1, wherein the sample is a plasma, serum, sweat, breath,tears, saliva, urine, stool, amniotic fluid, or cerebral spinal fluidsample.
 4. The method of claim 1, wherein the sample is not a pap smear,cyst fluid, or pancreatic fluid sample.
 5. The method of claim 1,wherein the sequence information comprises information related to atleast 2, 3, 5, 8, 10, 20, 30, 40, 100, 200, or 300 genomic regions. 6.The method of claim 5, wherein the genomic regions comprise two or moreof exonic regions, intronic regions, and untranslated regions.
 7. Themethod of claim 5, wherein the genomic regions comprise less than 1.5megabases (Mb), 1 Mb, 500 kb, 350 kb, 100 kb, 75 kb, 50 kb or 25 kb ofthe genome.
 8. The method of claim 1, wherein the sequence informationcomprises information pertaining to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions from aselector set comprising a plurality of genomic regions.
 9. The method ofclaim 8, wherein the plurality of genomic regions are based on aselector set comprising genomic regions comprising one or more mutationspresent in one or more subjects from a population of cancer subjects.10. The method of claim 8, wherein at least about 5%, 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or95% of the plurality of genomic regions are based on a selector setcomprising genomic regions comprising one or more mutations present inone or more subjects from a population of cancer subjects.
 11. Themethod of claim 9 or 10, wherein the selector set comprises 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or moregenomic regions selected from any one of Tables 2 and
 18. 12. The methodof claim 1, wherein the obtaining sequence information of step (a)comprises performing massively parallel sequencing.
 13. The method ofclaim 1, wherein the obtaining sequence information of step (a)comprises using one or more adaptors.
 14. The method of claim 13,wherein the one or more adaptors comprise a molecular barcode comprisinga randomer sequence.
 15. The method of claim 1, wherein using thesequence information of step (b) comprises detecting one or more ofSNVs, indels, copy number variants, and rearrangements in selectedregions of the subject's genome.
 16. The method of claim 1, whereinusing the sequence information of step (b) comprises detecting two ormore of SNVs, indels, copy number variants, and rearrangements inselected regions of the subject's genome.
 17. The method of claim 1,wherein the detecting of step (b) does not involve performing digitalPCR (dPCR).
 18. The method of claim 1, wherein the detecting of step (b)comprises applying an algorithm to the sequence information to determinea quantity of one or more genomic regions from a selector set.
 19. Themethod of claim 1, further comprising detecting, diagnosing, prognosingor selecting a therapy for a cancer in the subject based on thedetection of ctDNA.
 20. The method of claim 19, wherein diagnosing orprognosing the cancer has a sensitivity of at least about 50%, 52%, 55%,57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%,90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.
 21. The method of claim19, wherein diagnosing or prognosing the cancer has a specificity of atleast about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%,80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.22. A method of producing a selector set for a cancer comprising: (a)identifying genomic regions comprising mutations in one or more subjectsfrom a population of subjects suffering from the cancer; (b) ranking thegenomic regions based on a Recurrence Index (RI), wherein the RI of thegenomic region is determined by dividing the number of subjects ortumors with mutations in the genomic region by the size of the genomicregion; and (c) producing a selector set based on the RI.
 23. The methodof claim 22, wherein at least a subset of the genomic regions are exonregions, intron regions, untranslated regions, or a combination thereof.24. The method of claim 22, wherein producing the selector set based onthe RI comprises selecting genomic regions that have a recurrence indexin the top 70^(th), 75^(th), 80^(th), 85^(th), 90^(th), or 95^(th) orgreater percentile.
 25. The method of claim 22, wherein producing theselector set comprises applying an algorithm to a subset of the rankedgenomic regions.
 26. The method of claim 22, wherein producing theselector set comprises selecting genomic regions that maximize a mediannumber of mutations per subject of the selector set.
 27. The method ofclaim 22, wherein producing the selector set comprises selecting genomicregions that maximize the number of subjects in the selector set. 28.The method of claim 22, wherein producing the selector set comprisesselecting genomic regions that minimize the total size of the genomicregions.
 29. A computer readable medium comprising sequence informationfor two or more genomic regions wherein: (a) the two or more genomicregions comprise one or more mutations present in greater than or equalto 80% of tumors from a first population of subjects suffering from afirst type of cancer; (b) the two or more genomic regions represent lessthan 1.5 Mb of the genome; and (c) one or more of the following: (i) thecondition is not hairy cell leukemia, ovarian cancer, Waldenstrom'smacroglobulinemia; (ii) a genomic region comprises at least one mutationin at least one subject afflicted with the cancer; (iii) the two or moregenomic regions comprise one or more mutations present in a secondpopulation of subjects suffering from a second type of cancer; (iv) thetwo or more genomic regions are derived from two or more differentgenes; (v) the genomic regions comprise two or more mutations; or (vi)the two or more genomic regions comprise at least 10 kb.
 30. Thecomputer readable medium of claim 29, wherein the genomic regionscomprise one or more mutations present in greater than or equal to 60%of tumors from the second population of subjects suffering from thesecond type of cancer.
 31. The computer readable medium of claim 29,wherein the genomic regions are derived from 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70,80, 90, 100 or more different genes.
 32. The computer readable medium ofclaim 29, wherein the genomic regions comprise at least 1, 5, 10, 15,20, 25, 30, 35, 40, 45, or 50 kb.
 33. The computer readable medium ofclaim 29, wherein the sequence information comprises genomic coordinatespertaining to the two or more genomic regions.
 34. The computer readablemedium of claim 29, wherein the sequence information comprises a nucleicacid sequence pertaining to the two or more genomic regions.
 35. Thecomputer readable medium of claim 29, wherein the sequence informationcomprises a length of the two or more genomic regions.
 36. A compositioncomprising a set of oligonucleotides that selectively hybridize to aplurality of genomic regions, wherein: (a) greater than or equal to 80%of tumors from a population of cancer subjects include one or moremutations in the genomic regions; (b) the plurality of genomic regionsrepresent less than 1.5 Mb of the genome; and (c) the set ofoligonucleotides comprise 5 or more different oligonucleotides thatselectively hybridize to the plurality of genomic DNA regions.
 37. Thecomposition of claim 36, wherein the genomic DNA regions comprise atleast 2 regions from those identified in any one of Tables 2 and 6-18.38. The composition of claim 36, wherein the set of oligonucleotideshybridize to between about 5 kb to 1000 kb of the genome.
 39. Thecomposition of claim 36, wherein the set of oligonucleotides are capableof hybridizing to 5 or more different genomic regions.
 40. Thecomposition of claim 36, wherein the oligonucleotides are attached to asolid support.
 41. The composition of claim 40, wherein the solidsupport is a bead.
 42. The composition of claim 40, wherein the solidsupport is an array.
 43. A method for preparing a library for sequencingcomprising: (a) conducting an amplification reaction on cell-free DNA(cfDNA) derived from a sample to produce a plurality of amplicons,wherein the amplification reaction comprises 20 or fewer amplificationcycles; and (b) producing a library for sequencing, the librarycomprising the plurality of amplicons.
 44. The method of claim 43,wherein the amplification reaction comprises 15 or fewer amplificationcycles.
 45. The method of claim 43, further comprising attachingadaptors to the cell-free DNA.
 46. The method of claim 45, wherein theadaptors comprise a molecular barcode.
 47. The method of claim 45,wherein the adaptors comprise a sample index.
 48. The method of claim45, wherein the adaptors comprise a primer sequence.
 49. The method ofclaim 45, wherein the adaptors comprise a Y-shaped adaptor.
 50. Themethod of claim 43, further comprising fragmenting the cfDNA.
 51. Themethod of claim 43, further comprising end-repairing the cfDNA.
 52. Themethod of claim 43, further comprising A-tailing the cfDNA.
 53. A methodof determining a statistical significance of a selector set, the methodcomprising: (a) detecting a presence of one or more mutations in one ormore samples from a subject, wherein the one or more mutations are basedon a selector set comprising genomic regions comprising the one or moremutations; (b) determining a mutation type of the one or more mutationspresent in the sample; and (c) determining a statistical significance ofthe selector set by calculating a ctDNA detection index based on ap-value of the mutation type of mutations present in the one or moresamples.
 54. The method of claim 53, wherein if a rearrangement isobserved in two or more samples from the subject, then the ctDNAdetection index is
 0. 55. The method of claim 54, wherein at least oneof the two or more samples is a plasma sample.
 56. The method of claim54, wherein at least one of the two or more samples is a tumor sample.57. The method of claim 54, wherein the rearrangement is a fusion or abreakpoint.
 58. The method of claim 53, wherein if one type of mutationis present, then the ctDNA detection index is the p-value of the onetype of mutation.
 59. The method of claim 53, wherein if: (i) two ormore types of mutations are present in the sample; (ii) the p-values ofthe two or more types mutations are less than 0.1; and (iii) arearrangement is not one of the types of mutations, then the ctDNAdetection is calculated based on the combined p-values of the two ormore mutations.
 60. The method of claim 59, wherein the p-values of thetwo or more mutations are combined according to Fisher's method.
 61. Themethod of claim 59, wherein one of the two or more types of mutations isa SNV.
 62. The method of claim 61, wherein the p-value of the SNV isdetermined by Monte Carlo sampling.
 63. The method of claim 59, whereinone of the two or more types of mutations is an indel.
 64. The method ofclaim 53, wherein if: (i) two or more types of mutations are present inthe sample; (ii) a p-value of at least one of the two or more types ofmutations are greater than 0.1; and (iii) a rearrangement is not one ofthe types of mutations, then the ctDNA detection is calculated based onthe p-value of one of the two or more types mutations.
 65. The method ofclaim 64, wherein one of the two or more types of mutations is a SNV.66. The method of claim 65, wherein the ctDNA detection index iscalculated based on the p-value of the SNV.
 67. The method of claim 64,wherein one of the two or more types of mutations is an indel.
 68. Amethod of identifying rearrangements in one or more nucleic acids, themethod comprising: (a) obtaining sequencing information pertaining to aplurality of genomic regions; (b) producing a list of genomic regions,wherein the genomic regions are adjacent to one or more candidaterearrangement sites or the genomic regions comprise one or morecandidate rearrangement sites; (c) applying an algorithm to the list ofgenomic regions to validate candidate rearrangement sites, therebyidentifying rearrangements.
 69. The method of claim 68, wherein thesequencing information comprises an alignment file.
 70. The method ofclaim 69, wherein the alignment file comprises an alignment file ofpair-end reads, exon coordinates, and a reference genome.
 71. The methodof claim 68, wherein the sequencing information is obtained from adatabase.
 72. The method of claim 68, wherein the sequencing informationis obtained from one or more samples from one or more subjects.
 73. Themethod of claim 68, wherein producing the list of genomic regionscomprises identifying discordant read pairs based on the sequencinginformation.
 74. The method of claim 73, wherein producing the list ofgenomic regions comprises classifying the discordant read pairs based onthe sequencing information.
 75. The method of claim 73, whereinproducing the list of genomic regions further comprises ranking thegenomic regions.
 76. The method of claim 75, wherein the genomic regionsare ranked in decreasing order of discordant read depth.
 77. The methodof claim 68, wherein producing the list of genomic regions comprisesusing an algorithm to analyze properly paired reads in which one of thepaired reads is truncated to produce a soft-clipped read.
 78. The methodof claim 68, wherein the algorithm analyzes the soft-clipped reads basedon a pattern.
 79. The method of claim 78, wherein the pattern is basedon x number of skipped bases (Sx) and on y number of contiguous mappedbases (My).
 80. The method of claim 79, wherein the pattern is MySx orSxMy.
 81. The method of claim 68, wherein applying the algorithm tovalidate the candidate rearrangement sites comprises ranking thecandidate rearrangements based on their read frequency.
 82. The methodof claim 68, wherein applying the algorithm to validate the candidaterearrangement sites comprises comparing two or more reads of thecandidate rearrangement.
 83. The method of claim 82, wherein applyingthe algorithm to validate the candidate rearrangement sites comprisesidentifying the candidate rearrangement as a rearrangement if the two ormore reads have a sequence alignment.
 84. A method of identifyingtumor-derived single nucleotide variations (SNVs), the methodcomprising: (a) obtaining a sample from a subject suffering from acancer or suspected of suffering from a cancer; (b) conducting asequencing reaction on the sample to produce sequencing information; (c)applying an algorithm to the sequencing information to produce a list ofcandidate tumor alleles based on the sequencing information from step(b), wherein a candidate tumor allele comprises a non-dominant base thatis not a germline SNP; and (d) identifying tumor-derived SNVs based onthe list of candidate tumor alleles.
 85. The method of claim 84, whereinproducing the list of candidate tumor alleles comprises ranking thetumor alleles by their fractional abundance.
 86. The method of claim 85,wherein producing the list of candidate tumor alleles comprises rankingthe tumor alleles based on a sequencing depth.
 87. The method of claim86, wherein producing the list of candidate tumor alleles comprisesselecting tumor alleles that meet a minimum sequencing depth.
 88. Themethod of claim 87, wherein the minimum sequencing depth is at least100×, 200×, 300×, 400×, 500×, 600×, 700×, 800×, 900×, 1000× or more. 89.A method of producing a selector set comprising: (a) obtainingsequencing information of a tumor sample from a subject suffering from acancer; (b) comparing the sequencing information of the tumor sample tosequencing information from a non-tumor sample from the subject toidentify one or more mutations specific to the sequencing information ofthe tumor sample; and (c) producing a selector set comprising one ormore genomic regions comprising the one or more mutations specific tothe sequencing information of the tumor sample.
 90. The method of claim89, wherein the selector set comprises sequencing information pertainingto the one or more genomic regions.
 91. The method of claim 90, whereinthe selector set comprises genomic coordinates pertaining to the one ormore genomic regions.
 92. The method of claim 90, wherein the selectorset comprises a plurality of oligonucleotides that selectively hybridizethe one or more genomic regions.
 93. The method of claim 92, wherein theplurality of oligonucleotides are biotinylated.
 94. The method of claim89, the one or more mutations comprise SNVs, indels, rearrangements, ora combination thereof.
 95. The method of claim 94, wherein producing theselector set comprises identifying tumor-derived SNVs based on themethod of any one of claims 84-88.
 96. The method of claim 94, whereinproducing the selector set comprises identifying tumor-derivedrearrangements based on the method of any one of claims 68-83.